Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
entity-linking-classification
Languages:
English
Size:
< 1K
License:
id stringlengths 8 8 | title stringlengths 18 138 | abstract stringlengths 177 1.96k | entities list | relation list |
|---|---|---|---|---|
H01-1001 | Activity detection for information access to oral communication | Oral communication is ubiquitous and carries important information yet it is also time consuming to document. Given the development of storage media and networks one could just record and store a conversation for documentation. The question is, however, how an interesting information piece would be found in a large da... | [
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H01-1017 | Dialogue Interaction with the DARPA Communicator Infrastructure: The Development of Useful Software | To support engaging human users in robust, mixed-initiative speech dialogue interactions which reach beyond current capabilities in dialogue systems , the DARPA Communicator program [1] is funding the development of a distributed message-passing infrastructure for dialogue systems which all Communicator participants a... | [
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H01-1041 | Interlingua-Based Broad-Coverage Korean-to-English Translation in CCLING | At MIT Lincoln Laboratory, we have been developing a Korean-to-English machine translation system CCLINC (Common Coalition Language System at Lincoln Laboratory) . The CCLINC Korean-to-English translation system consists of two core modules , language understanding and generation modules mediated by a language neutral... | [
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H01-1042 | Is That Your Final Answer? | The purpose of this research is to test the efficacy of applying automated evaluation techniques , originally devised for the evaluation of human language learners , to the output of machine translation (MT) systems . We believe that these evaluation techniques will provide information about both the human language le... | [
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H01-1049 | Listen-Communicate-Show (LCS): Spoken Language Command of Agent-based Remote Information Access | Listen-Communicate-Show (LCS) is a new paradigm for human interaction with data sources . We integrate a spoken language understanding system with intelligent mobile agents that mediate between users and information sources . We have built and will demonstrate an application of this approach called LCS-Marine . Using ... | [
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H01-1058 | On Combining Language Models : Oracle Approach | In this paper, we address the problem of combining several language models (LMs) . We find that simple interpolation methods , like log-linear and linear interpolation , improve the performance but fall short of the performance of an oracle . The oracle knows the reference word string and selects the word string with ... | [
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H01-1070 | Towards an Intelligent Multilingual Keyboard System | This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction and Thai-English language identification . The paper also proposes rule-reduction algorithm applying mutual information to reduce the error-correction rules . Our algorithm reported more than 99% accurac... | [
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N01-1003 | SPoT: A Trainable Sentence Planner | Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping , i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences . In this paper, we present SPoT , a sentence planner , and a new methodology for autom... | [
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P01-1004 | Low-cost, High-performance Translation Retrieval: Dumber is Better | In this paper, we compare the relative effects of segment order , segmentation and segment contiguity on the retrieval performance of a translation memory system . We take a selection of both bag-of-words and segment order-sensitive string comparison methods , and run each over both character- and word-segmented data ... | [
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P01-1007 | Guided Parsing of Range Concatenation Languages | The theoretical study of the range concatenation grammar [RCG] formalism has revealed many attractive properties which may be used in NLP . In particular, range concatenation languages [RCL] can be parsed in polynomial time and many classical grammatical formalisms can be translated into equivalent RCGs without increa... | [
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P01-1008 | Extracting Paraphrases from a Parallel Corpus | While paraphrasing is critical both for interpretation and generation of natural language , current systems use manual or semi-automatic methods to collect paraphrases . We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source tex... | [
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P01-1009 | Alternative Phrases and Natural Language Information Retrieval | This paper presents a formal analysis for a large class of words called alternative markers , which includes other (than) , such (as) , and besides . These words appear frequently enough in dialog to warrant serious attention , yet present natural language search engines perform poorly on queries containing them. I sh... | [
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P01-1047 | Extending Lambek grammars: a logical account of minimalist grammars | We provide a logical definition of Minimalist grammars , that are Stabler's formalization of Chomsky's minimalist program . Our logical definition leads to a neat relation to categorial grammar , (yielding a treatment of Montague semantics ), a parsing-as-deduction in a resource sensitive logic , and a learning algori... | [
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P01-1056 | Evaluating a Trainable Sentence Planner for a Spoken Dialogue System | Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches . In this paper We experimentally evaluate a... | [
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P01-1070 | Using Machine Learning Techniques to Interpret WH-questions | We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions . These models , which are built from shallow linguistic features of questions , are employed to predict target variables which represent a user's informational goals . We report on differen... | [
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N03-1001 | Effective Utterance Classification with Unsupervised Phonotactic Models | This paper describes a method for utterance classification that does not require manual transcription of training data . The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using convention... | [
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N03-1004 | In Question Answering, Two Heads Are Better Than One | Motivated by the success of ensemble methods in machine learning and other areas of natural language processing , we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora . The answeri... | [
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N03-1012 | Semantic Coherence Scoring Using an Ontology | In this paper we present ONTOSCORE , a system for scoring sets of concepts on the basis of an ontology . We apply our system to the task of scoring alternative speech recognition hypotheses (SRH) in terms of their semantic coherence . We conducted an annotation experiment and showed that human annotators can reliably ... | [
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N03-1017 | Statistical Phrase-Based Translation | We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models . Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based m... | [
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N03-1018 | A Generative Probabilistic OCR Model for NLP Applications | In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework , progressing from generation of true text through its transformation into the noisy output of an OCR system . The model is designed for use in error corr... | [
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N03-1026 | Statistical Sentence Condensation using Ambiguity Packing and Stochastic Disambiguation Methods for Lexical-Functional Grammar | We present an application of ambiguity packing and stochastic disambiguation techniques for Lexical-Functional Grammars (LFG) to the domain of sentence condensation . Our system incorporates a linguistic parser/generator for LFG , a transfer component for parse reduction operating on packed parse forests , and a maxim... | [
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N03-1033 | Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network | We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation , (ii) broad use of lexical features , including jointly conditioning on multiple consecutive words , (iii) effective use of priors in cond... | [
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N03-2003 | Getting More Mileage from Web Text Sources for Conversational Speech Language Modeling using Class-Dependent Mixtures | Sources of training data suitable for language modeling of conversational speech are limited. In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task , but also that it is possible to get bigger performance gains from t... | [
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N03-2006 | Adaptation Using Out-of-Domain Corpus within EBMT | In order to boost the translation quality of EBMT based on a small-sized bilingual corpus , we use an out-of-domain bilingual corpus and, in addition, the language model of an in-domain monolingual corpus . We conducted experiments with an EBMT system . The two evaluation measures of the BLEU score and the NIST score ... | [
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N03-2015 | Unsupervised Learning of Morphology for English and Inuktitut | We describe a simple unsupervised technique for learning morphology by identifying hubs in an automaton . For our purposes, a hub is a node in a graph with in-degree greater than one and out-degree greater than one. We create a word-trie , transform it into a minimal DFA , then identify hubs . Those hubs mark the boun... | [
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N03-2017 | Word Alignment with Cohesion Constraint | We present a syntax-based constraint for word alignment , known as the cohesion constraint . It requires disjoint English phrases to be mapped to non-overlapping intervals in the French sentence . We evaluate the utility of this constraint in two different algorithms. The results show that it can provide a significant... | [
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N03-2025 | Bootstrapping for Named Entity Tagging Using Concept-based Seeds | A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE , e.g. he/she/man/woman for PERSON NE . The bootstrapping procedure is implemente... | [
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N03-2036 | A Phrase-Based Unigram Model for Statistical Machine Translation | In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parameters than similar phrase-based models . The units of translation are blocks - pairs of phrases . During decoding , we use a block unigram model and a word-based trigram language model... | [
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N03-3010 | Cooperative Model Based Language Understanding in Dialogue | In this paper, we propose a novel Cooperative Model for natural language understanding in a dialogue system . We build this based on both Finite State Model (FSM) and Statistical Learning Model (SLM) . FSM provides two strategies for language understanding and have a high accuracy but little robustness and flexibility... | [
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N03-4010 | JAVELIN: A Flexible, Planner-Based Architecture for Question Answering | The JAVELIN system integrates a flexible, planning-based architecture with a variety of language processing modules to provide an open-domain question answering capability on free text . The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidates from the given text ... | [
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P03-1002 | Using Predicate-Argument Structures for Information Extraction | In this paper we present a novel, customizable : IE paradigm that takes advantage of predicate-argument structures . We also introduce a new way of automatically identifying predicate argument structures , which is central to our IE paradigm . It is based on: (1) an extended set of features ; and (2) inductive decisio... | [
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P03-1005 | Hierarchical Directed Acyclic Graph Kernel: Methods for Structured Natural Language Data | This paper proposes the Hierarchical Directed Acyclic Graph (HDAG) Kernel for structured natural language data . The HDAG Kernel directly accepts several levels of both chunks and their relations , and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs . We applied the p... | [
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P03-1009 | Clustering Polysemic Subcategorization Frame Distributions Semantically | Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated corpus data . We describe a new approach which involves clustering subcategorization frame (SCF) distributions using the Information Bottleneck and nearest neighbour methods. In contrast to previous work... | [
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P03-1022 | A Machine Learning Approach to Pronoun Resolution in Spoken Dialogue | We apply a decision tree based approach to pronoun resolution in spoken dialogue . Our system deals with pronouns with NP- and non-NP-antecedents . We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features . We evaluate the system on twenty Switchboard di... | [
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P03-1030 | Optimizing Story Link Detection is not Equivalent to Optimizing New Event Detection | Link detection has been regarded as a core technology for the Topic Detection and Tracking tasks of new event detection . In this paper we formulate story link detection and new event detection as information retrieval task and hypothesize on the impact of precision and recall on both systems. Motivated by these argum... | [
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P03-1031 | Corpus-based Discourse Understanding in Spoken Dialogue Systems | This paper concerns the discourse understanding process in spoken dialogue systems . This process enables the system to understand user utterances based on the context of a dialogue . Since multiple candidates for the understanding result can be obtained for a user utterance due to the ambiguity of speech understandin... | [
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P03-1033 | Flexible Guidance Generation using User Model in Spoken Dialogue Systems | We address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems . Unlike previous studies that focus on user 's knowledge or typical kinds of users , the user model we propose is more comprehensive. Specifically, we set up three dimensions of user models : skill ... | [
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P03-1050 | Unsupervised Learning of Arabic Stemming using a Parallel Corpus | This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer . The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources . No parallel text is needed after the training pha... | [
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P03-1051 | Language Model Based Arabic Word Segmentation | We approximate Arabic's rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme ). Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Ar... | [
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P03-1058 | Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical Study |
A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning . In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora , which are then used for disambiguating the nouns in the S... | [
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P03-1068 | Towards a Resource for Lexical Semantics: A Large German Corpus with Extensive Semantic Annotation | We describe the ongoing construction of a large, semantically annotated corpus resource as reliable basis for the large-scale acquisition of word-semantic information , e.g. the construction of domain-independent lexica . The backbone of the annotation are semantic roles in the frame semantics paradigm . We report exp... | [
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P03-1070 | Towards a Model of Face-to-Face Grounding | We investigate the verbal and nonverbal means for grounding , and propose a design for embodied conversational agents that relies on both kinds of signals to establish common ground in human-computer interaction . We analyzed eye gaze , head nods and attentional focus in the context of a direction-giving task . The di... | [
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P03-2036 | Comparison between CFG filtering techniques for LTAG and HPSG | An empirical comparison of CFG filtering techniques for LTAG and HPSG is presented. We demonstrate that an approximation of HPSG produces a more effective CFG filter than that of LTAG . We also investigate the reason for that difference. | [
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C04-1106 | Lower and higher estimates of the number of &quot;true analogies&quot; between sentences contained in a large multilingual corpus | The reality of analogies between words is refuted by noone (e.g., I walked is to to walk as I laughed is to to laugh, noted I walked : to walk :: I laughed : to laugh). But computational linguists seem to be quite dubious about analogies between sentences : they would not be enough numerous to be of any use. We report... | [
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N04-1024 | Evaluating Multiple Aspects of Coherence in Student Essays | CriterionSM Online Essay Evaluation Service includes a capability that labels sentences in student writing with essay-based discourse elements (e.g., thesis statements ). We describe a new system that enhances Criterion 's capability, by evaluating multiple aspects of coherence in essays . This system identifies featu... | [
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H05-1005 | Improving Multilingual Summarization: Using Redundancy in the Input to Correct MT errors | In this paper, we use the information redundancy in multilingual input to correct errors in machine translation and thus improve the quality of multilingual summaries . We consider the case of multi-document summarization , where the input documents are in Arabic , and the output summary is in English . Typically, inf... | [
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H05-1012 | A Maximum Entropy Word Aligner for Arabic-English Machine Translation | This paper presents a maximum entropy word alignment algorithm for Arabic-English based on supervised training data . We demonstrate that it is feasible to create training material for problems in machine translation and that a mixture of supervised and unsupervised methods yields superior performance . The probabilis... | [
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H05-1095 | Translating with non-contiguous phrases | This paper presents a phrase-based statistical machine translation method , based on non-contiguous phrases , i.e. phrases with gaps. A method for producing such phrases from a word-aligned corpora is proposed. A statistical translation model is also presented that deals such phrases , as well as a training method bas... | [
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H05-1117 | Automatically Evaluating Answers to Definition Questions | Following recent developments in the automatic evaluation of machine translation and document summarization , we present a similar approach, implemented in a measure called POURPRE , for automatically evaluating answers to definition questions . Until now, the only way to assess the correctness of answers to such ques... | [
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H05-2007 | Pattern Visualization for Machine Translation Output | We describe a method for identifying systematic patterns in translation data using part-of-speech tag sequences . We incorporate this analysis into a diagnostic tool intended for developers of machine translation systems , and demonstrate how our application can be used by developers to explore patterns in machine tra... | [
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I05-2021 | Evaluating the Word Sense Disambiguation Performance of Statistical Machine Translation | We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly on word sense disambiguation performance , using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task . Much effort has... | [
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I05-2048 | Statistical Machine Translation Part I: Hands-On Introduction | Statistical machine translation (SMT) is currently one of the hot spots in natural language processing . Over the last few years dramatic improvements have been made, and a number of comparative evaluations have shown, that SMT gives competitive results to rule-based translation systems , requiring significantly less ... | [
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I05-4010 | Harvesting the Bitexts of the Laws of Hong Kong From the Web | In this paper we present our recent work on harvesting English-Chinese bitexts of the laws of Hong Kong from the Web and aligning them to the subparagraph level via utilizing the numbering system in the legal text hierarchy . Basic methodology and practical techniques are reported in detail. The resultant bilingual co... | [
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I05-5003 | Using Machine Translation Evaluation Techniques to Determine Sentence-level Semantic Equivalence | The task of machine translation (MT) evaluation is closely related to the task of sentence-level semantic equivalence classification . This paper investigates the utility of applying standard MT evaluation methods (BLEU, NIST, WER and PER) to building classifiers to predict semantic equivalence and entailment . We als... | [
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I05-5008 | Automatic generation of paraphrases to be used as translation references in objective evaluation measures of machine translation | We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST . We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality : at least 99% correct sentenc... | [
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I05-6011 | Annotating Honorifics Denoting Social Ranking of Referents | This paper proposes an annotating scheme that encodes honorifics (respectful words). Honorifics are used extensively in Japanese , reflecting the social relationship (e.g. social ranks and age) of the referents . This referential information is vital for resolving zero pronouns and improving machine translation output... | [
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J05-1003 | Discriminative Reranking for Natural Language Parsing | This article considers approaches which rerank the output of an existing probabilistic parser . The base parser produces a set of candidate parses for each input sentence , with associated probabilities that define an initial ranking of these parses . A second model then attempts to improve upon this initial ranking ,... | [
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J05-4003 | Improving Machine Translation Performance by Exploiting Non-Parallel Corpora | We present a novel method for discovering parallel sentences in comparable, non-parallel corpora . We train a maximum entropy classifier that, given a pair of sentences , can reliably determine whether or not they are translations of each other. Using this approach, we extract parallel data from large Chinese, Arabic... | [
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P05-1032 | Scaling Phrase-Based Statistical Machine Translation to Larger Corpora and Longer Phrases | In this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memory than is required by current decoder implementations. We detail the computational complexity and average retrieval times for... | [
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P05-1034 | Dependency Treelet Translation: Syntactically Informed Phrasal SMT | We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation . This method requires a source-language dependency parser , target language word segmentation and an unsupervised word alignment component . We align a... | [
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P05-1048 | Word Sense Disambiguation vs. Statistical Machine Translation | We directly investigate a subject of much recent debate: do word sense disambigation models help statistical machine translation quality ? We present empirical results casting doubt on this common, but unproved, assumption. Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidat... | [
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P05-1067 | Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars | Syntax-based statistical machine translation (MT) aims at applying statistical models to structured data . In this paper, we present a syntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar . Synchronous dependency insertion grammars are a version of synch... | [
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P05-1069 | A Localized Prediction Model for Statistical Machine Translation | In this paper, we present a novel training method for a localized phrase-based prediction model for statistical machine translation (SMT) . The model predicts blocks with orientation to handle local phrase re-ordering . We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valu... | [
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P05-1074 | Paraphrasing with Bilingual Parallel Corpora | Previous work has used monolingual parallel corpora to extract and generate paraphrases . We show that this task can be done using bilingual parallel corpora , a much more commonly available resource . Using alignment techniques from phrase-based statistical machine translation , we show how paraphrases in one languag... | [
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P05-2016 | Dependency-Based Statistical Machine Translation | We present a Czech-English statistical machine translation system which performs tree-to-tree translation of dependency structures . The only bilingual resource required is a sentence-aligned parallel corpus . All other resources are monolingual . We also refer to an evaluation method and plan to compare our system's ... | [
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E06-1018 | Word Sense Induction: Triplet-Based Clustering and Automatic Evaluation | In this paper a novel solution to automatic and unsupervised word sense induction (WSI) is introduced. It represents an instantiation of the one sense per collocation observation (Gale et al., 1992). Like most existing approaches it utilizes clustering of word co-occurrences . This approach differs from other approach... | [
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E06-1022 | Addressee Identification in Face-to-Face Meetings | We present results on addressee identification in four-participants face-to-face meetings using Bayesian Network and Naive Bayes classifiers . First, we investigate how well the addressee of a dialogue act can be predicted based on gaze , utterance and conversational context features . Then, we explore whether informa... | [
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E06-1031 | CDER: Efficient MT Evaluation Using Block Movements | Most state-of-the-art evaluation measures for machine translation assign high costs to movements of word blocks. In many cases though such movements still result in correct or almost correct sentences . In this paper, we will present a new evaluation measure which explicitly models block reordering as an edit operatio... | [
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E06-1035 | Automatic Segmentation of Multiparty Dialogue | In this paper, we investigate the problem of automatically predicting segment boundaries in spoken multiparty dialogue . We extend prior work in two ways. We first apply approaches that have been proposed for predicting top-level topic shifts to the problem of identifying subtopic boundaries . We then explore the impa... | [
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P06-1013 | Ensemble Methods for Unsupervised WSD | Combination methods are an effective way of improving system performance . This paper examines the benefits of system combination for unsupervised WSD . We investigate several voting- and arbiter-based combination strategies over a diverse pool of unsupervised WSD systems . Our combination methods rely on predominant ... | [
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P06-1052 | An Improved Redundancy Elimination Algorithm for Underspecified Representations | We present an efficient algorithm for the redundancy elimination problem : Given an underspecified semantic representation (USR) of a scope ambiguity , compute an USR with fewer mutually equivalent readings . The algorithm operates on underspecified chart representations which are derived from dominance graphs ; it ca... | [
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P06-2001 | Using Machine Learning Techniques to Build a Comma Checker for Basque | In this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in a grammar checker for Basque . After several experiments, and trained with a little corpus of 100,000 words , the system guesses correctly not placing commas with a precision of 96% and a recall of 98... | [
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P06-2012 | Unsupervised Relation Disambiguation Using Spectral Clustering | This paper presents an unsupervised learning approach to disambiguate various relations between named entities by use of various lexical and syntactic features from the contexts . It works by calculating eigenvectors of an adjacency graph 's Laplacian to recover a submanifold of data from a high dimensionality space a... | [
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P06-2059 | Automatic Construction of Polarity-tagged Corpus from HTML Documents | This paper proposes a novel method of building polarity-tagged corpus from HTML documents . The characteristics of this method is that it is fully automatic and can be applied to arbitrary HTML documents . The idea behind our method is to utilize certain layout structures and linguistic pattern . By using them, we can... | [
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H01-1040 | Intelligent Access to Text: Integrating Information Extraction Technology into Text Browsers |
In this paper we show how two standard outputs from information extraction (IE) systems - named entity annotations and scenario templates - can be used to enhance access to text collections via a standard text browser . We describe how this information is used in a prototype system designed to support information wo... | [
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H01-1055 | Natural Language Generation in Dialog Systems |
Recent advances in Automatic Speech Recognition technology have put the goal of naturally sounding dialog systems within reach. However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding ... | [
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H01-1068 | A Three-Tiered Evaluation Approach for Interactive Spoken Dialogue Systems |
We describe a three-tiered approach for evaluation of spoken dialogue systems . The three tiers measure user satisfaction , system support of mission success and component performance . We describe our use of this approach in numerous fielded user studies conducted with the U.S. military. | [
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N03-4004 | TAP-XL: An Automated Analyst's Assistant |
The TAP-XL Automated Analyst's Assistant is an application designed to help an English -speaking analyst write a topical report , culling information from a large inflow of multilingual, multimedia data . It gives users the ability to spend their time finding more data relevant to their task, and gives them translin... | [
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H05-1101 | Some Computational Complexity Results for Synchronous Context-Free Grammars |
This paper investigates some computational problems associated with probabilistic translation models that have recently been adopted in the literature on machine translation . These models can be viewed as pairs of probabilistic context-free grammars working in a 'synchronous' way. Two hardness results for the class... | [
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I05-2014 | BLEU in characters: towards automatic MT evaluation in languages without word delimiters |
Automatic evaluation metrics for Machine Translation (MT) systems , such as BLEU or NIST , are now well established. Yet, they are scarcely used for the assessment of language pairs like English-Chinese or English-Japanese , because of the word segmentation problem . This study establishes the equivalence between th... | [
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P05-3025 | Interactively Exploring a Machine Translation Model |
This paper describes a method of interactively visualizing and directing the process of translating a sentence . The method allows a user to explore a model of syntax-based statistical machine translation (MT) , to understand the model 's strengths and weaknesses, and to compare it to other MT systems . Using this v... | [
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E06-1004 | Computational Complexity of Statistical Machine Translation |
In this paper we study a set of problems that are of considerable importance to Statistical Machine Translation (SMT) but which have not been addressed satisfactorily by the SMT research community . Over the last decade, a variety of SMT algorithms have been built and empirically tested whereas little is known about... | [
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E06-1041 | Structuring Knowledge for Reference Generation: A Clustering Algorithm |
This paper discusses two problems that arise in the Generation of Referring Expressions : (a) numeric-valued attributes , such as size or location; (b) perspective-taking in reference . Both problems, it is argued, can be resolved if some structure is imposed on the available knowledge prior to content determination... | [
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N06-2009 | Answering the Question You Wish They Had Asked: The Impact of Paraphrasing for Question Answering |
State-of-the-art Question Answering (QA) systems are very sensitive to variations in the phrasing of an information need . Finding the preferred language for such a need is a valuable task. We investigate that claim by adopting a simple MT-based paraphrasing technique and evaluating QA system performance on paraphra... | [
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N06-2038 | A Comparison of Tagging Strategies for Statistical Information Extraction |
There are several approaches that model information extraction as a token classification task , using various tagging strategies to combine multiple tokens . We describe the tagging strategies that can be found in the literature and evaluate their relative performances. We also introduce a new strategy, called Begin... | [
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N06-4001 | InfoMagnets: Making Sense of Corpus Data |
We introduce a new interactive corpus exploration tool called InfoMagnets . InfoMagnets aims at making exploratory corpus analysis accessible to researchers who are not experts in text mining . As evidence of its usefulness and usability, it has been used successfully in a research context to uncover relationships b... | [
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P06-1018 | Polarized Unification Grammars |
This paper proposes a generic mathematical formalism for the combination of various structures : strings , trees , dags , graphs , and products of them. The polarization of the objects of the elementary structures controls the saturation of the final structure . This formalism is both elementary and powerful enough ... | [
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P06-2110 | Word Vectors and Two Kinds of Similarity |
This paper examines what kind of similarity between words can be represented by what kind of word vectors in the vector space model . Through two experiments, three methods for constructing word vectors , i.e., LSA-based, cooccurrence-based and dictionary-based methods , were compared in terms of the ability to repr... | [
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P06-3007 | Investigations on Event-Based Summarization |
We investigate independent and relevant event-based extractive mutli-document summarization approaches . In this paper, events are defined as event terms and associated event elements . With independent approach, we identify important contents by frequency of events . With relevant approach, we identify important co... | [
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P06-4007 | FERRET: Interactive Question-Answering for Real-World Environments |
This paper describes FERRET , an interactive question-answering (Q/A) system designed to address the challenges of integrating automatic Q/A applications into real-world environments. FERRET utilizes a novel approach to Q/A known as predictive questioning which attempts to identify the questions (and answers ) that ... | [
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] |
P06-4011 | Computational Analysis of Move Structures in Academic Abstracts |
This paper introduces a method for computational analysis of move structures in abstracts of research articles . In our approach, sentences in a given abstract are analyzed and labeled with a specific move in light of various rhetorical functions . The method involves automatically gathering a large number of abstra... | [
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"arg1": "P06-4011.8",
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{
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... |
P06-4014 | Re-Usable Tools for Precision Machine Translation |
The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes on output quality . The demonstrator embodies an interesting combination of hand-built, symbolic resources and stochastic processes . | [
{
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] |
T78-1001 | Testing The Psychological Reality of a Representational Model |
A research program is described in which a particular representational format for meaning is tested as broadly as possible. In this format, developed by the LNR research group at The University of California at San Diego, verbs are represented as interconnected sets of subpredicates . These subpredicates may be thou... | [
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{
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"arg1": "T78-1001.10",
"arg2": "T78-1001.11",
"reverse": true
}
] |
T78-1028 | Fragments of a Theory of Human Plausible Reasoning |
The paper outlines a computational theory of human plausible reasoning constructed from analysis of people's answers to everyday questions. Like logic , the theory is expressed in a content-independent formalism . Unlike logic , the theory specifies how different information in memory affects the certainty of the co... | [
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T78-1031 | PATH-BASED AND NODE-BASED INFERENCE IN SEMANTIC NETWORKS |
Two styles of performing inference in semantic networks are presented and compared. Path-based inference allows an arc or a path of arcs between two given nodes to be inferred from the existence of another specified path between the same two nodes . Path-based inference rules may be written using a binary relational... | [
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C80-1039 | ON FROFF: A TEXT PROCESSING SYSTEM FOR ENGLISH TEXTS AND FIGURES |
In order to meet the needs of a publication of papers in English, many systems to run off texts have been developed. In this paper, we report a system FROFF which can make a fair copy of not only texts but also graphs and tables indispensable to our papers. Its selection of fonts , specification of character size ar... | [
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C80-1073 | ATNS USED AS A PROCEDURAL DIALOG MODEL |
An attempt has been made to use an Augmented Transition Network as a procedural dialog model . The development of such a model appears to be important in several respects: as a device to represent and to use different dialog schemata proposed in empirical conversation analysis ; as a device to represent and to use m... | [
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P80-1004 | Metaphor - A Key to Extensible Semantic Analysis |
Interpreting metaphors is an integral and inescapable process in human understanding of natural language . This paper discusses a method of analyzing metaphors based on the existence of a small number of generalized metaphor mappings . Each generalized metaphor contains a recognition network , a basic mapping , addi... | [
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P80-1019 | Expanding the Horizons of Natural Language Interfaces |
Current natural language interfaces have concentrated largely on determining the literal meaning of input from their users . While such decoding is an essential underpinning, much recent work suggests that natural language interfaces will never appear cooperative or graceful unless they also incorporate numerous non... | [
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P80-1026 | Flexiable Parsing |
When people use natural language in natural settings, they often use it ungrammatically, missing out or repeating words, breaking-off and restarting, speaking in fragments, etc.. Their human listeners are usually able to cope with these deviations with little difficulty. If a computer system wishes to accept natural ... | [
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{
"label": 1,
"arg1": "P80-1026.8",
"arg2": "P80-1026.9",
"reverse": false
}
] |
End of preview. Expand in Data Studio
Dataset Card for SemEval2018Task7
Dataset Summary
Semeval2018Task7 is a dataset that describes the Semantic Relation Extraction and Classification in Scientific Papers. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.
The three subtasks are:
Subtask 1.1: Relation classification on clean data
- In the training data, semantic relations are manually annotated between entities.
- In the test data, only entity annotations and unlabeled relation instances are given.
- Given a scientific publication, The task is to predict the semantic relation between the entities.
Subtask 1.2: Relation classification on noisy data
- Entity occurrences are automatically annotated in both the training and the test data.
- The task is to predict the semantic relation between the entities.
Subtask 2: Metrics for the extraction and classification scenario
- Evaluation of relation extraction
- Evaluation of relation classification
The Relations types are USAGE, RESULT, MODEL, PART_WHOLE, TOPIC, COMPARISION.
The following example shows a text snippet with the information provided in the test data: Korean, a <entity id=”H01-1041.10”>verb final language</entity>with<entity id=”H01-1041.11”>overt case markers</entity>(...)
- A relation instance is identified by the unique identifier of the entities in the pair, e.g.(H01-1041.10, H01-1041.11)
- The information to be predicted is the relation class label: MODEL-FEATURE(H01-1041.10, H01-1041.11). For details, see the paper https://aclanthology.org/S18-1111/.
Supported Tasks and Leaderboards
- Tasks: Relation extraction and classification in scientific papers
- Leaderboards: https://competitions.codalab.org/competitions/17422#learn_the_details-overview
Languages
The language in the dataset is English.
Dataset Structure
Data Instances
subtask_1.1
- Size of downloaded dataset files: 714 KB
An example of 'train' looks as follows:
{
"id": "H01-1041",
"title": "'Interlingua-Based Broad-Coverage Korean-to-English Translation in CCLING'",
"abstract": 'At MIT Lincoln Laboratory, we have been developing a Korean-to-English machine translation system CCLINC (Common Coalition Language System at Lincoln Laboratory) . The CCLINC Korean-to-English translation system consists of two core modules , language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame . The key features of the system include: (i) Robust efficient parsing of Korean (a verb final language with overt case markers , relatively free word order , and frequent omissions of arguments ). (ii) High quality translation via word sense disambiguation and accurate word order generation of the target language . (iii) Rapid system development and porting to new domains via knowledge-based automated acquisition of grammars . Having been trained on Korean newspaper articles on missiles and chemical biological warfare, the system produces the translation output sufficient for content understanding of the original document.
"entities": [{'id': 'H01-1041.1', 'char_start': 54, 'char_end': 97},
{'id': 'H01-1041.2', 'char_start': 99, 'char_end': 161},
{'id': 'H01-1041.3', 'char_start': 169, 'char_end': 211},
{'id': 'H01-1041.4', 'char_start': 229, 'char_end': 240},
{'id': 'H01-1041.5', 'char_start': 244, 'char_end': 288},
{'id': 'H01-1041.6', 'char_start': 304, 'char_end': 342},
{'id': 'H01-1041.7', 'char_start': 353, 'char_end': 366},
{'id': 'H01-1041.8', 'char_start': 431, 'char_end': 437},
{'id': 'H01-1041.9', 'char_start': 442, 'char_end': 447},
{'id': 'H01-1041.10', 'char_start': 452, 'char_end': 470},
{'id': 'H01-1041.11', 'char_start': 477, 'char_end': 494},
{'id': 'H01-1041.12', 'char_start': 509, 'char_end': 523},
{'id': 'H01-1041.13', 'char_start': 553, 'char_end': 561},
{'id': 'H01-1041.14', 'char_start': 584, 'char_end': 594},
{'id': 'H01-1041.15', 'char_start': 600, 'char_end': 624},
{'id': 'H01-1041.16', 'char_start': 639, 'char_end': 659},
{'id': 'H01-1041.17', 'char_start': 668, 'char_end': 682},
{'id': 'H01-1041.18', 'char_start': 692, 'char_end': 715},
{'id': 'H01-1041.19', 'char_start': 736, 'char_end': 742},
{'id': 'H01-1041.20', 'char_start': 748, 'char_end': 796},
{'id': 'H01-1041.21', 'char_start': 823, 'char_end': 847},
{'id': 'H01-1041.22', 'char_start': 918, 'char_end': 935},
{'id': 'H01-1041.23', 'char_start': 981, 'char_end': 997}],
}
"relation": [{'label': 3, 'arg1': 'H01-1041.3', 'arg2': 'H01-1041.4', 'reverse': True},
{'label': 0, 'arg1': 'H01-1041.8', 'arg2': 'H01-1041.9', 'reverse': False},
{'label': 2, 'arg1': 'H01-1041.10', 'arg2': 'H01-1041.11', 'reverse': True},
{'label': 0, 'arg1': 'H01-1041.14', 'arg2': 'H01-1041.15', 'reverse': True}]
Subtask_1.2
- Size of downloaded dataset files: 1.00 MB
An example of 'train' looks as follows:
{'id': 'L08-1450',
'title': '\nA LAF/GrAF based Encoding Scheme for underspecified Representations of syntactic Annotations.\n',
'abstract': 'Data models and encoding formats for syntactically annotated text corpora need to deal with syntactic ambiguity; underspecified representations are particularly well suited for the representation of ambiguousdata because they allow for high informational efficiency. We discuss the issue of being informationally efficient, and the trade-off between efficient encoding of linguistic annotations and complete documentation of linguistic analyses. The main topic of this article is adata model and an encoding scheme based on LAF/GrAF ( Ide and Romary, 2006 ; Ide and Suderman, 2007 ) which provides a flexible framework for encoding underspecified representations. We show how a set of dependency structures and a set of TiGer graphs ( Brants et al., 2002 ) representing the readings of an ambiguous sentence can be encoded, and we discuss basic issues in querying corpora which are encoded using the framework presented here.\n',
'entities': [{'id': 'L08-1450.4', 'char_start': 0, 'char_end': 3},
{'id': 'L08-1450.5', 'char_start': 5, 'char_end': 10},
{'id': 'L08-1450.6', 'char_start': 25, 'char_end': 31},
{'id': 'L08-1450.7', 'char_start': 61, 'char_end': 64},
{'id': 'L08-1450.8', 'char_start': 66, 'char_end': 72},
{'id': 'L08-1450.9', 'char_start': 82, 'char_end': 85},
{'id': 'L08-1450.10', 'char_start': 92, 'char_end': 100},
{'id': 'L08-1450.11', 'char_start': 102, 'char_end': 110},
{'id': 'L08-1450.12', 'char_start': 128, 'char_end': 142},
{'id': 'L08-1450.13', 'char_start': 181, 'char_end': 194},
{'id': 'L08-1450.14', 'char_start': 208, 'char_end': 211},
{'id': 'L08-1450.15', 'char_start': 255, 'char_end': 264},
{'id': 'L08-1450.16', 'char_start': 282, 'char_end': 286},
{'id': 'L08-1450.17', 'char_start': 408, 'char_end': 420},
{'id': 'L08-1450.18', 'char_start': 425, 'char_end': 443},
{'id': 'L08-1450.19', 'char_start': 450, 'char_end': 453},
{'id': 'L08-1450.20', 'char_start': 455, 'char_end': 459},
{'id': 'L08-1450.21', 'char_start': 481, 'char_end': 484},
{'id': 'L08-1450.22', 'char_start': 486, 'char_end': 490},
{'id': 'L08-1450.23', 'char_start': 508, 'char_end': 513},
{'id': 'L08-1450.24', 'char_start': 515, 'char_end': 519},
{'id': 'L08-1450.25', 'char_start': 535, 'char_end': 537},
{'id': 'L08-1450.26', 'char_start': 559, 'char_end': 561},
{'id': 'L08-1450.27', 'char_start': 591, 'char_end': 598},
{'id': 'L08-1450.28', 'char_start': 611, 'char_end': 619},
{'id': 'L08-1450.29', 'char_start': 649, 'char_end': 663},
{'id': 'L08-1450.30', 'char_start': 687, 'char_end': 707},
{'id': 'L08-1450.31', 'char_start': 722, 'char_end': 726},
{'id': 'L08-1450.32', 'char_start': 801, 'char_end': 808},
{'id': 'L08-1450.33', 'char_start': 841, 'char_end': 845},
{'id': 'L08-1450.34', 'char_start': 847, 'char_end': 852},
{'id': 'L08-1450.35', 'char_start': 857, 'char_end': 864},
{'id': 'L08-1450.36', 'char_start': 866, 'char_end': 872},
{'id': 'L08-1450.37', 'char_start': 902, 'char_end': 910},
{'id': 'L08-1450.1', 'char_start': 12, 'char_end': 16},
{'id': 'L08-1450.2', 'char_start': 27, 'char_end': 32},
{'id': 'L08-1450.3', 'char_start': 72, 'char_end': 80}],
'relation': [{'label': 1,
'arg1': 'L08-1450.12',
'arg2': 'L08-1450.13',
'reverse': False},
{'label': 5, 'arg1': 'L08-1450.17', 'arg2': 'L08-1450.18', 'reverse': False},
{'label': 1, 'arg1': 'L08-1450.28', 'arg2': 'L08-1450.29', 'reverse': False},
{'label': 3, 'arg1': 'L08-1450.30', 'arg2': 'L08-1450.32', 'reverse': False},
{'label': 3, 'arg1': 'L08-1450.34', 'arg2': 'L08-1450.35', 'reverse': False},
{'label': 3, 'arg1': 'L08-1450.36', 'arg2': 'L08-1450.37', 'reverse': True}]}
[ ]
Data Fields
subtask_1_1
id: the instance id of this abstract, astringfeature.title: the title of this abstract, astringfeatureabstract: the abstract from the scientific papers, astringfeatureentities: the entity id's for the key phrases, alistof entity id's.id: the instance id of this sentence, astringfeature.char_start: the 0-based index of the entity starting, anìntfeature.char_end: the 0-based index of the entity ending, anìntfeature.
relation: the list of relations of this sentence marking the relation between the key phrases, alistof classification labels.label: the list of relations between the key phrases, alistof classification labels.arg1: the entity id of this key phrase, astringfeature.arg2: the entity id of the related key phrase, astringfeature.reverse: the reverse isTrueonly if reverse is possible otherwiseFalse, aboolfeature.
RELATIONS
{"":0,"USAGE": 1, "RESULT": 2, "MODEL-FEATURE": 3, "PART_WHOLE": 4, "TOPIC": 5, "COMPARE": 6}
subtask_1_2
id: the instance id of this abstract, astringfeature.title: the title of this abstract, astringfeatureabstract: the abstract from the scientific papers, astringfeatureentities: the entity id's for the key phrases, alistof entity id's.id: the instance id of this sentence, astringfeature.char_start: the 0-based index of the entity starting, anìntfeature.char_end: the 0-based index of the entity ending, anìntfeature.
relation: the list of relations of this sentence marking the relation between the key phrases, alistof classification labels.label: the list of relations between the key phrases, alistof classification labels.arg1: the entity id of this key phrase, astringfeature.arg2: the entity id of the related key phrase, astringfeature.reverse: the reverse isTrueonly if reverse is possible otherwiseFalse, aboolfeature.
RELATIONS
{"":0,"USAGE": 1, "RESULT": 2, "MODEL-FEATURE": 3, "PART_WHOLE": 4, "TOPIC": 5, "COMPARE": 6}
Data Splits
| Train | Test | ||
|---|---|---|---|
| subtask_1_1 | text | 2807 | 3326 |
| relations | 1228 | 1248 | |
| subtask_1_2 | text | 1196 | 1193 |
| relations | 335 | 355 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@inproceedings{gabor-etal-2018-semeval,
title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
author = {G{\'a}bor, Kata and
Buscaldi, Davide and
Schumann, Anne-Kathrin and
QasemiZadeh, Behrang and
Zargayouna, Ha{\"\i}fa and
Charnois, Thierry},
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1111",
doi = "10.18653/v1/S18-1111",
pages = "679--688",
abstract = "This paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.",
}
Contributions
Thanks to @basvoju for adding this dataset.
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