DOCSLIB.ORG

Survey of Textual Entailment Approaches, Rakesh

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Survey of Textual Entailment Approaches, Rakesh Survey of Textual Entailment Approaches Rakesh Khobragade, Heaven Patel and Pushpak Bhattacharyya Indian Institute of Technology, Bombay frkhobrag, heaven, [email protected] Abstract competition, two text fragments are given and the task is to determine if one can be inferred from Natural Language Inference, or Textual En- the other. There was a total of 7 RTE chal- tailment, has applications in many NLP tasks lenges from 2005-2011. RTE 1-3 focused on 2- such as question-answering, text summariza- tion, paraphrase detection, and machine trans- way classification including only Entailment and lation evaluation. In this survey, we perform Non-entailment classes. From RTE 4, the neu- an analysis of developments in the NLI tasks. tral class was also introduced making RTE as a We discuss various approaches that have been 3-way classification task. This addition of neutral proposed and datasets available to train deep class hardened the RTE task but also made it pos- learning models. We also describe a few ap- sible for systems to be able to distinguish between plications of NLI. contradictory and unknown premise and hypoth- esis pairs. RTE challenges also included Summa- 1 Introduction rization and novelty detection tasks apart from En- Textual Entailment, or Natural Language Infer- tailment task to increase the difficulty and make ence, is considered as one of the major tasks in challenges more realistic. RTE challenges have re- Natural Language Processing(NLP) that requires sulted in the development of benchmarks for NLI deep semantic understanding. For a pair of sen- task and have uncovered the challenges faced in tences, textual entailment can be defined as the recognizing textual entailment. task of finding if one of them can be inferred from the other. Consider Example 1 where we can in- 3 Classical Methods fer hypothesis “John knows how to drive” from the premise “John cleared driving test”. Entail- The basic solution to Recognizing Entailment is ment task needs a semantic understanding, mod- to compare the re-represented version of premise els trained on the entailment data can be applied to and hypothesis and determine if the hypothesis can many other NLP tasks such as text summarization, be derived from the premise based on the com- paraphrase detection, and machine translation. We parison. We can re-represent the sentences us- can note that the inference relation in Entailment ing either lexical or syntactical methods. The lex- is a unidirectional relation only from premise to ical method works directly on the input surface hypothesis. If we also have Entailment from hy- strings by operating solely on a string comparison pothesis to premise, we can say that both sentences between the text and the hypothesis. Lexical ap- convey the meaning. proach ignores the semantic relationship between premise and hypothesis and determines the entail- Example 1 John took driving test today and ment based on only lexical concepts. Most of the cleared it =) John knows how to drive common approaches in lexical methods are word 2 RTE Challenges overlapping, subsequence matching, etc. Syntax- based or syntactic approaches convert premise and RTE(Recognizing Textual Entailment) challenge, hypothesis into directed graphs. These graphs can started in 2005 (Dagan et al., 2005), is an an- be created using a parse tree and then compared nual competition where researchers from the NLP with each other. Entailment or Non-Entailment community showcase entailment models. In this is determined using the comparison between the graphs of premise and hypothesis. Entailment can SNLI corpus as it tries to add more diversity in be recognized using different rules and tasks. In the types of sentences. MNLI is a crowd-sourced Example 2, we need to know following things to collection of 433k sentence pairs annotated identify entailment; “Cisco Systems Inc.” is same with textual entailment information. It contains as “Cisco” (Entity Matching), “Filed a lawsuit” sentence pairs from 10 different genres, out of is same as “accused” (Paraphrasing), Eliminate which only 5 appears in the training set while all “last year” from the text (Alignment task), “Cisco 10 appear in the test set. This makes the learning accused Apple” is different from “Apple accused NLI more challenging and generic. Cisco” (Semantic Role Labeling). Example 2 “Cisco Systems Inc. filed a lawsuit 4.3 XNLI against Apple for a patent violation last year.” Cross-lingual NLI(XNLI) (Conneau et al., 2018) =) “Cisco accused Apple for patent violation.” corpus is created intended to encourage research in 4 Datasets cross-linguality. It is derived from the MNLI cor- pus for 15 languages using crowd-sourcing. Dev RTE challenges provided initial datasets to test and test sets of MNLI are manually translated to and train NLI models. But these were very limited 15 languages: English, French, Spanish, German, in quantity, comprising of only a few thousand of Greek, Bulgarian, Russian, Turkish, Arabic, Viet- sentence pairs. As neural networks architectures namese, Thai, Chinese, Hindi, Swahili, and Urdu. became more powerful, attempts were made to Out of these Urdu and Swahili are low-resource create a large corpus that can be used for training languages. of deep neural networks. The accuracy achieved on these datasets is considered as the benchmark 4.4 SciTail to compare different NLI models. In this section, Although SNLI has proved to be beneficial for we look at some of the datasets that enabled mod- the advancement of techniques for recognizing els to learn to predict correctly by looking at many textual entailment and has provided researchers examples. to run many deep neural models on but it has been observed the dataset is not very effective 4.1 SNLI for training a model for a particular end task like Stanford Natural Language Inference(SNLI) question-answering. SCITAIL (Khot et al., 2018) (Bowman et al., 2015) corpus is the first one to is created with end-task of question-answering in have presented the research community a platform view. to test their neural network models on. SNLI is created using crowdsourcing, using Amazon Mechanical Turks platform. In this process, human volunteers were presented with captions of images and were asked to construct three sentences, one in favor of subject of the image, one in contradiction to it and one unrelated to the subject of the image. This created the hypothesis belonging to three classes entailment, contradic- tion and neutral. The premise was constructed by another crowdsourced task in which volunteers were asked to caption images from Flickr40k (Young et al., 2014). It has about 30k images and 160k of total captions were acquired. Overall there are total 550,152 training pairs, and 10k each in development and test set. 4.2 MNLI Figure 1: An example from Scitail dataset Multi-genre Natural Language Inference(MNLI) (Williams et al., 2018) is an improvement over the The premise-hypothesis pairs are created from a corpus of high school level science related mul- similarity task. It includes around 400,000 ques- tiple choice questions. The hypothesis is created tions duplicate examples. Each line has ID for the by combining the question and the correct answer. Question pair, individual ids for question pair, two The premise is obtained independently from a cor- separate questions, and label of whether they are pus of web text such that it entails the hypothesis. duplicate or not. Similarly, premises are created for contradiction Training data includes 255027 non-duplicates and neutral pairs. Since the premise and hypoth- and 149263 duplicates whereas Testing data on the esis are created independently they are very dif- Kaggle platform includes 2345796 question pairs. ferent from each other in terms of the syntactic and lexical structure. This makes the task of en- 5 Approaches tailment more challenging since there can be sen- tences that do not have a huge overlap of words but A lot about the meaning of a sentence can be are similar in meaning and at the same time there inferred from its lexical and syntactic composi- can sentences with many words overlapping but tion. Features like the presence of negation or are not related. The annotation of such premise- synonyms can be used to compare sentences. hypothesis pair as supports (entails) or not (neu- Dependency graph can also be used to understand tral), is crowdsourced in order to create the Sci- how different entities interact with each other. Tail dataset. The dataset contains 27,026 exam- But because of the variability and ambiguity of ples with 10,101 examples with entails label and natural language, semantics cannot be ignored. In 16,925 examples with a neutral label. this section, we first look at an approach using machine learning and then move on to more 4.5 MRPC recent neural network models. MRPC paraphrase corpus (Dolan and Brockett, With the advent of SNLI and MNLI corpus, it 2005) consists of 5800 paraphrase sentence pairs became possible to run deep learning models for with a label(Paraphrase or Non-paraphrase). This NLI. Large data which comprises of many exam- corpus is collected from the web news sources in ples of differing composition allows discovery of which 67% of the examples in the corpus are pos- features that would otherwise be difficult to iden- itive whereas only 33% examples are negative. In tify. Prior to deep learning, most of the approaches MRPC dataset, around 4100 pairs are training ex- made use of hand-crafted features, mostly distance amples and 1700 pairs are test examples. metrics, to train the NLI models. This lead to a very restricted set of features that could be used 4.6 Glockner corpus while training. Also, since different languages Glockner dataset (Glockner et al., 2018) was have a different composition of sentences, manu- created using SNLI dataset for the sole purpose ally extracting various language features is not fea- of measuring the lexical similarity of a model.
Load more

Recommended publications
  • Are Annotators' Word-Sense-Disambiguation
    J. Hlavácovᡠ(Ed.): ITAT 2017 Proceedings, pp. 5–14 CEUR Workshop Proceedings Vol. 1885, ISSN 1613-0073, c 2017 S. Cinková, A. Vernerová Are Annotators’ Word-Sense-Disambiguation Decisions Affected by Textual Entailment between Lexicon Glosses? Silvie Cinková, Anna Vernerová Charles University, Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Malostranské námestíˇ 25 118 00 Praha 1 Czech Republic ufal.mff.cuni.cz {cinkova,vernerova}@ufal.mff.cuni.cz Abstract: We describe an annotation experiment com- verb on the one hand, and those related to the lexicograph- bining topics from lexicography and Word Sense Disam- ical design of PDEV, such as semantic relations between biguation. It involves a lexicon (Pattern Dictionary of En- implicatures within a lemma or denotative similarity of the glish Verbs, PDEV), an existing data set (VPS-GradeUp), verb arguments, on the other hand (see Section 3 for defi- and an unpublished data set (RTE in PDEV Implicatures). nitions and examples). The aim of the experiment was twofold: a pilot annota- This paper focuses on a feature related to PDEV’s de- tion of Recognizing Textual Entailment (RTE) on PDEV sign (see Section 2.3), namely on textual entailment be- implicatures (lexicon glosses) on the one hand, and, on tween implicatures in pairs of patterns of the same lemma the other hand, an analysis of the effect of Textual Entail- entry (henceforth colempats, see Section 3.1 for definition ment between lexicon glosses on annotators’ Word-Sense- and more detail). Disambiguation decisions, compared to other predictors, We pairwise compare all colempats, examining their such as finiteness of the target verb, the explicit presence scores in the graded decision annotation with respect to of its relevant arguments, and the semantic distance be- how much they compete to become the most appropriate tween corresponding syntactic arguments in two different pattern, as well as the scores of presence of textual entail- patterns (dictionary senses).
    [Show full text]
  • Textual Inference for Machine Comprehension Martin Gleize
    Textual Inference for Machine Comprehension Martin Gleize To cite this version: Martin Gleize. Textual Inference for Machine Comprehension. Computation and Language [cs.CL]. Université Paris Saclay (COmUE), 2016. English. NNT : 2016SACLS004. tel-01317577 HAL Id: tel-01317577 https://tel.archives-ouvertes.fr/tel-01317577 Submitted on 18 May 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Le cas échéant, logo de l’établissement co-délivrant le doctorat en cotutelle internationale de thèse , sinon mettre le logo de l’établissement de préparation de la thèse (UPSud, HEC, UVSQ, UEVE, ENS Cachan, Polytechnique, IOGS, …) NNT : 2016SACLS004 THÈSE DE DOCTORAT DE L’UNIVERSITÉ PARIS-SACLAY PRÉPARÉE À L'UNIVERSITÉ PARIS-SUD ECOLE DOCTORALE N° 580 Sciences et technologies de l'information et de la communication (STIC) Spécialité de doctorat : Informatique Par M. Martin Gleize Textual Inference for Machine Comprehension Thèse présentée et soutenue à Orsay, le 7 janvier 2016 Composition du Jury : M. Yvon François Professeur, Université Paris-Sud Président, Examinateur Mme Gardent Claire Directeur de recherche CNRS, LORIA Rapporteur M. Magnini Bernardo Senior Researcher, FBK Rapporteur M. Piwowarski Benjamin Chargé de recherche CNRS, LIP6 Examinateur Mme Grau Brigitte Professeur, ENSIIE Directeur de thèse Abstract With the ever-growing mass of published text, natural language understanding stands as one of the most sought-after goal of artificial intelligence.
    [Show full text]
  • Repurposing Entailment for Multi-Hop Question Answering Tasks
    Repurposing Entailment for Multi-Hop Question Answering Tasks Harsh Trivedi|, Heeyoung Kwon|, Tushar Khot♠, Ashish Sabharwal♠, Niranjan Balasubramanian| | Stony Brook University, Stony Brook, U.S.A. fhjtrivedi,heekwon,[email protected] ♠ Allen Institute for Artificial Intelligence, Seattle, U.S.A. ftushark,[email protected] Abstract Question Answering (QA) naturally reduces to an entailment problem, namely, verifying whether some text entails the answer to a ques- tion. However, for multi-hop QA tasks, which require reasoning with multiple sentences, it remains unclear how best to utilize entailment models pre-trained on large scale datasets such as SNLI, which are based on sentence pairs. We introduce Multee, a general architecture Figure 1: An example illustrating the challenges in us- that can effectively use entailment models for ing sentence-level entailment model for multi-sentence multi-hop QA tasks. Multee uses (i) a local reasoning needed for QA, and the high-level approach module that helps locate important sentences, used in Multee. thereby avoiding distracting information, and level, whereas question answering requires verify- (ii) a global module that aggregates informa- ing whether multiple sentences, taken together as tion by effectively incorporating importance weights. Importantly, we show that both mod- a premise, entail a hypothesis. ules can use entailment functions pre-trained There are two straightforward ways to address on a large scale NLI datasets. We evaluate per- this mismatch: (1) aggregate independent entail- formance on MultiRC and OpenBookQA, two ment decisions over each premise sentence, or (2) multihop QA datasets. When using an entail- make a single entailment decision after concate- ment function pre-trained on NLI datasets, nating all premise sentences.
    [Show full text]
  • Techniques for Recognizing Textual Entailment and Semantic Equivalence
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by EPrints Complutense Techniques for Recognizing Textual Entailment and Semantic Equivalence Jes´us Herrera, Anselmo Pe˜nas, Felisa Verdejo Departamento de Lenguajes y Sistemas Inform´aticos Universidad Nacional de Educaci´ona Distancia Madrid, Spain {jesus.herrera, anselmo, felisa}@lsi.uned.es Abstract. After defining what is understood by textual entailment and semantic equivalence, the present state and the desirable future of the systems aimed at recognizing them is shown. A compilation of the currently implemented techniques in the main Recognizing Textual Entailment and Semantic Equivalence systems is given. 1 Introduction The concept “textual entailment” is used to indicate the state in which the semantics of a natural language written text can be inferred from the semantics of another one. More specifically, if the truth of an enunciation entails the truth of another enunciation. For example, given the texts: 1. The three-day G8 meeting will take place in Scotland. 2. The Group of Eight summit will last three days. it is clear that the semantics of the second one can be inferred from the semantics of the first one; then, it is said that textual entailment exists between both texts. Textual entailment is a directional relationship: in the example above, the first statement entails the second one, but this entailment is not given in the opposite direction. The recognition of textual entailment requires a processing at the lexical level (for example, synonymy between meeting and summit or between G8 and Group of Eight), as well as at the syntactic level and the sentence semantic level.
    [Show full text]
  • Automatic Evaluation of Summary Using Textual Entailment
    Automatic Evaluation of Summary Using Textual Entailment Pinaki Bhaskar Partha Pakray Department of Computer Science & Department of Computer Science & Engineering, Jadavpur University, Engineering, Jadavpur University, Kolkata – 700032, India Kolkata – 700032, India [email protected] [email protected] summary is very much needed when a large Abstract number of summaries have to be evaluated, spe- cially for multi-document summaries. For sum- This paper describes about an automatic tech- mary evaluation we have developed an automat- nique of evaluating summary. The standard ed evaluation technique based on textual entail- and popular summary evaluation techniques or ment. tools are not fully automatic; they all need Recognizing Textual Entailment (RTE) is one some manual process. Using textual entail- of the recent research areas of Natural Language ment (TE) the generated summary can be evaluated automatically without any manual Processing (NLP). Textual Entailment is defined evaluation/process. The TE system is the as a directional relationship between pairs of text composition of lexical entailment module, lex- expressions, denoted by the entailing “Text” (T) ical distance module, Chunk module, Named and the entailed “Hypothesis” (H). T entails H if Entity module and syntactic text entailment the meaning of H can be inferred from the mean- (TE) module. The syntactic TE system is ing of T. Textual Entailment has many applica- based on the Support Vector Machine (SVM) tions in NLP tasks, such as Summarization, In- that uses twenty five features for lexical simi- formation Extraction, Question Answering, In- larity, the output tag from a rule based syntac- formation Retrieval. tic two-way TE system as a feature and the outputs from a rule based Chunk Module and 2 Related Work Named Entity Module as the other features.
    [Show full text]
  • Dependency-Based Textual Entailment
    Dependency-based Textual Entailment Vasile Rus Department of Computer Science Institute for Intelligent Systems The University of Memphis Memphis, TN 38120, USA [email protected] Abstract and Hypothesis are mapped into two sets of dependen- cies: the T-set and H-set, respectively. A score is then This paper studies the role of dependency information computed that measures the degree to which the depen- for the task of textual entailment. Both the Text and Hypothesis of an entailment pair are mapped into sets dencies in the H-set are present in the T-set. Based on of dependencies and a score is computed that measures the score, an entailment decision is made. Dependen- the similarity of the two sets. Based on the score an cies are labelled relations among words in a sentence. entailment decision is made. Two experiments are con- For example, there is a subj dependency between cap- ducted to measure the impact of dependencies on the ital and Besancon in the previous example which we entailment task. In one experiment we compare the represent as the triplet (subj, capital, Besancon). The dependency-based approach with a baseline approach first term in the triplet is the name or label of the re- on a standard data set. In a second experiment, we lation, the second term represents the head word, and measure the performance on a subset of the standard the last term is the dependent word. We use MINIPAR data set. The subset is so selected to minimize the (Lin 1998), an automated dependency parser, as our effect of other factors, such as word-level information, in the performance measurement process.
    [Show full text]
  • Parser Evaluation Using Textual Entailments
    Language Resources and Evaluation manuscript No. (will be inserted by the editor) Parser Evaluation Using Textual Entailments Deniz Yuret · Laura Rimell · Aydın Han Received: date / Accepted: date Abstract Parser Evaluation using Textual Entailments (PETE) is a shared task in the SemEval-2010 Evaluation Exercises on Semantic Evaluation. The task involves recognizing textual entailments based on syntactic information alone. PETE introduces a new parser evaluation scheme that is formalism independent, less prone to annotation error, and focused on semantically rele- vant distinctions. This paper describes the PETE task, gives an error analysis of the top-performing Cambridge system, and introduces a standard entail- ment module that can be used with any parser that outputs Stanford typed dependencies. Keywords Parsing · Textual Entailments 1 Introduction Parser Evaluation using Textual Entailments (PETE) is a shared task in the SemEval-2010 Evaluation Exercises on Semantic Evaluation that involves rec- ognizing textual entailments to evaluate parser performance. Given two text Deniz Yuret and Aydın Han Ko¸cUniversity Rumelifeneri Yolu 34450 Sarıyer, Istanbul,˙ Turkey Tel.: +90-212-338-1724 Fax: +90-212-338-1548 E-mail: dyuret,[email protected] Laura Rimell Computer Laboratory William Gates Building 15 JJ Thomson Avenue Cambridge CB3 0FD, UK Tel.: +44 (0)1223 334696 E-mail: [email protected] 2 Deniz Yuret et al. fragments called “text” (T) and “hypothesis” (H), recognizing textual entail- ment (RTE) is the task of determining whether the meaning of the hypothesis is entailed (can be inferred) from the text. In contrast to general RTE tasks (Dagan et al 2009) PETE is a targeted textual entailment task that focuses on syntactic entailments: Text: The man with the hat was tired.
    [Show full text]
  • Textual Entailment Recognition Based on Dependency Analysis and Wordnet
    Textual Entailment Recognition Based on Dependency Analysis and WordNet Jesus´ Herrera, Anselmo Penas,˜ Felisa Verdejo Departamento de Lenguajes y Sistemas Informaticos´ Universidad Nacional de Educacion´ a Distancia Madrid, Spain jesus.herrera, anselmo, felisa @lsi.uned.es f g Abstract. The Recognizing Textual Entailment System shown here is based on the use of a broad-coverage parser to extract dependency relationships; in addi- tion, WordNet relations are used to recognize entailment at the lexical level. The work investigates whether the mapping of dependency trees from text and hy- pothesis give better evidence of entailment than the matching of plain text alone. While the use of WordNet seems to improve system's performance, the notion of mapping between trees here explored (inclusion) shows no improvement, sug- gesting that other notions of tree mappings should be explored such as tree edit distances or tree alignment distances. 1 Introduction Textual Entailment Recognition (RTE) aims at deciding whether the truth of a text en- tails the truth of another text called hypothesis. This concept has been the basis for the PASCAL1 RTE Challenge [3]. The system presented here is aimed at validating the hypothesis that (i) a certain amount of semantic information could be extracted from texts by means of the syntactic structure given by a dependency analysis, and that (ii) lexico-semantic information such as WordNet relations can improve RTE. In short, the techniques involved in this system are the following: – Dependency analysis of texts and hypothesises. – Lexical entailment between dependency tree nodes using WordNet. – Mapping between dependency trees based on the notion of inclusion.
    [Show full text]
  • A Probabilistic Classification Approach for Lexical Textual Entailment
    A Probabilistic Classification Approach for Lexical Textual Entailment Oren Glickman and Ido Dagan and Moshe Koppel Computer Science Department , Bar Ilan University Ramat-Gan, 52900, Iarael {glikmao, dagan, koppel}@cs.biu.ac.il Abstract The uncertain nature of textual entailment calls for its The textual entailment task – determining if a given text explicit modeling in probabilistic terms. In this paper we entails a given hypothesis – provides an abstraction of first propose a general generative probabilistic setting for applied semantic inference. This paper describes first a textual entailment. This probabilistic framework may be general generative probabilistic setting for textual considered analogous to (though different than) the entailment. We then focus on the sub-task of recognizing probabilistic setting defined for other phenomena, like whether the lexical concepts present in the hypothesis are language modeling and statistical machine translation. entailed from the text. This problem is recast as one of text Obviously, such frameworks are needed to allow for categorization in which the classes are the vocabulary principled (fuzzy) modeling of the relevant language words. We make novel use of Naïve Bayes to model the problem in an entirely unsupervised fashion. Empirical tests phenomenon, compared to utilizing ad-hoc ranking scores suggest that the method is effective and compares favorably which have no clear probabilistic interpretation. We with state-of-the-art heuristic scoring approaches. suggest that the proposed setting may provide a unifying framework for modeling uncertain semantic inferences from texts. Introduction An important sub task of textual entailment, which we term lexical entailment , is recognizing if the concepts in a Many Natural Language Processing (NLP) applications hypothesis h are entailed from a given text t, even if the need to recognize when the meaning of one text can be relations between these concepts may not be entailed from expressed by, or inferred from, another text.
    [Show full text]
  • Frame Semantics Annotation Made Easy with Dbpedia
    Frame Semantics Annotation Made Easy with DBpedia Marco Fossati, Sara Tonelli, and Claudio Giuliano ffossati, satonelli, [email protected] Fondazione Bruno Kessler - via Sommarive, 18 - 38123 Trento, Italy Abstract. Crowdsourcing techniques applied to natural language pro- cessing have recently experienced a steady growth and represent a cheap and fast, albeit valid, solution to create benchmarks and training data. Nevertheless, some particularly complex tasks such as semantic role an- notation have been rarely conducted in a crowdsourcing environment, due to their intrinsic difficulty. In this paper, we present a novel ap- proach to accomplish this task by leveraging information automatically extracted from DBpedia. We show that replacing role definitions, typi- cally meant for expert annotators, with a list of DBpedia types, makes the identification and assignment of role labels more intuitive also for non-expert workers. Results prove that such strategy improves on the standard annotation workflow, both in terms of accuracy and of time consumption. Keywords: Natural Language Processing, Frame Semantics, En- tity Linking, DBpedia, Crowdsourcing, Task Modeling 1 Introduction Frame semantics [6] is one of the theories that originate from the long strand of linguistic research in artificial intelligence. A frame can be informally defined as an event triggered by some term in a text and embedding a set of partic- ipants. For instance, the sentence Goofy has murdered Mickey Mouse evokes the Killing frame (triggered by murdered) together with the Killer and Vic- tim participants (respectively Goofy and Mickey Mouse). Such theory has led to the creation of FrameNet [2], namely an English lexical database containing manually annotated textual examples of frame usage.
    [Show full text]
  • Constructing a Textual Semantic Relation Corpus Using a Discourse Treebank
    Constructing a Textual Semantic Relation Corpus Using a Discourse Treebank Rui Wang and Caroline Sporleder Computational Linguistics Saarland University frwang,[email protected] Abstract In this paper, we present our work on constructing a textual semantic relation corpus by making use of an existing treebank annotated with discourse relations. We extract adjacent text span pairs and group them into six categories according to the different discourse relations between them. After that, we present the details of our annotation scheme, which includes six textual semantic relations, backward entailment, forward entailment, equality, contradiction, overlapping, and independent. We also discuss some ambiguous examples to show the difficulty of such annotation task, which cannot be easily done by an automatic mapping between discourse relations and semantic relations. We have two annotators and each of them performs the task twice. The basic statistics on the constructed corpus looks promising: we achieve 81.17% of agreement on the six semantic relation annotation with a .718 kappa score, and it increases to 91.21% if we collapse the last two labels with a .775 kappa score. 1. Introduction TRADICTION, and UNKNOWN. ENTAILMENT is the same Traditionally, the term ‘semantic relation’ refers to rela- as the previous YES; but NO is divided into CONTRADIC- tions that hold between the meanings of two words, e.g., TION and UNKNOWN, to differentiate cases where T and H synonymy, hypernymy, etc. These relations are usually are contradictory to each other from all the other cases. The situation-independent. Discourse relations, on the other RTE data are acquired from other natural language process- hand, tend to depend on the wider context of an utterance.
    [Show full text]
  • PATTY: a Taxonomy of Relational Patterns with Semantic Types
    PATTY: A Taxonomy of Relational Patterns with Semantic Types Ndapandula Nakashole, Gerhard Weikum, Fabian Suchanek Max Planck Institute for Informatics Saarbrucken,¨ Germany {nnakasho,weikum,suchanek}@mpi-inf.mpg.de Abstract Yet, existing large-scale knowledge bases are mostly limited to abstract binary relationships be- This paper presents PATTY: a large resource for textual patterns that denote binary relations tween entities, such as “bornIn” (Auer 2007; Bol- between entities. The patterns are semanti- lacker 2008; Nastase 2010; Suchanek 2007). These cally typed and organized into a subsumption do not correspond to real text phrases. Only the Re- taxonomy. The PATTY system is based on ef- Verb system (Fader 2011) yields a larger number of ficient algorithms for frequent itemset mining relational textual patterns. However, no attempt is and can process Web-scale corpora. It har- made to organize these patterns into synonymous nesses the rich type system and entity popu- patterns, let alone into a taxonomy. Thus, the pat- lation of large knowledge bases. The PATTY taxonomy comprises 350,569 pattern synsets. terns themselves do not exhibit semantics. Random-sampling-based evaluation shows a Goal. Our goal in this paper is to systematically pattern accuracy of 84.7%. PATTY has 8,162 compile relational patterns from a corpus, and to im- subsumptions, with a random-sampling-based pose a semantically typed structure on them. The precision of 75%. The PATTY resource is freely available for interactive access and result we aim at is a WordNet-style taxonomy of download. binary relations. In particular, we aim at patterns that contain semantic types, such as hsingeri sings 1 Introduction hsongi.
    [Show full text]