Textual entailment graphs

Kotlerman, Lili; Dagan, Ido; Magnini, Bernardo; Bentivogli, Luisa

doi:10.1017/s1351324915000108

Cited by 19 publications

(12 citation statements)

References 21 publications

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“…In order to verify the applicability of our method to a data set other than SICK, and to compare our results with several other existing systems, we used the EXCITEMENT english development data set (Kotlerman et al 2015), which is freely available from the EOP web site 3 . EXCITEMENT contains pieces of email feedback sent by the customers of a railway company where they state reasons for satisfaction or dissatisfaction with the company.…”

Section: Data Setsmentioning

confidence: 99%

A transformation-driven approach for recognizing textual entailment

Zanoli¹,

Colombo

2016

Nat. Lang. Eng.

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Textual Entailment is a directional relation between two text fragments. The relation holds whenever the truth of one text fragment, called Hypothesis (H), follows from another text fragment, called Text (T). Up until now, using machine learning approaches for recognizing textual entailment has been hampered by the limited availability of data. We present an approach based on syntactic transformations and machine learning techniques which is designed to fit well with a new type of available data sets that are larger but less complex than data sets used in the past. The transformations are not predefined, but calculated from the data sets, and then used as features in a supervised learning classifier. The method has been evaluated using two data sets: the SICK data set and the EXCITEMENT English data set. While both data sets are of a larger order of magnitude than data sets such as RTE-3, they are also of lower levels of complexity, each in its own way. SICK consists of pairs created by applying a predefined set of syntactic and lexical rules to its T and H pairs, which can be accurately captured by our transformations. The EXCITEMENT English data contains short pieces of text that do not require a high degree of text understanding to be annotated. The resulting AdArte system is simple to understand and implement, but also effective when compared with other existing systems. AdArte has been made freely available with the EXCITEMENT Open Platform, an open source platform for textual inference.

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Section: Data Setsmentioning

confidence: 99%

A transformation-driven approach for recognizing textual entailment

Zanoli¹,

Colombo

2016

Nat. Lang. Eng.

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“…This task corresponds to RTE-3, and the main difference to Evaluation 1 is that these pairs come from real-world interactions and were produced by native speakers. All T-H pairs are sampled from application gold data which were manually constructed on the basis of anonymized customer interactions for German; Kotlerman et al (2015) for English and Italian 2 ). The sets are fairly large (5300 pairs for English, 1700 for Italian, 1274 for German), and were sampled to be balanced.…”

Section: Evaluation 2: T-h Pairs From Application Datamentioning

confidence: 99%

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2015

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will take place in Denver on June 4 and 5 and is colocated with SemEval and NAACL. As in 2014 at COLING, also on this occasion *SEM and SemEval chose to coordinate their programs by featuring a joint invited talk. In this way, *SEM aims to bring together the ACL SIGLEX and ACL SIGSEM communities.The acceptance rate of *SEM 2015 was quite competitive: out of 98 submissions, we accepted 36 papers for an overall acceptance of 37%. The acceptance rate of long paper that were accepted for oral presentation (18 out of 62) is 29%. The papers cover a wide range of topics including distributional semantics; lexical semantics and lexical acquisition; formal and linguistic semantics; discourse semantics; lexical resources, linked data and ontologies; semantics for applications; and extra-propositional semantics: sentiment and figurative meaning.The *SEM 2015 program consists of oral presentations for selected long papers and a poster session for long and short papers.Day One, June 4th:• Joint *SEM SemEval keynote talk by Marco Baroni;• Oral presentation sessions on distributional semantics, lexical semantics, and extra-propositional semantics;• Poster session.Day Two, June 5th:• Keynote talk by Preslav Natkov;• Oral presentation sessions on semantics for applications, lexical resources and ontologies, formal semantics, and discourse semantics;• *SEM Best Paper Award.We cannot finish without saying that *SEM 2015 would not have been possible without the considerable efforts of our area chairs, their reviewers, and the computational semantics community in general.We hope you will enjoy *SEM 2015, Distributional semantic methods have some a priori appeal as models of human meaning acquisition, because they induce word representations from contextual distributions naturally occurring in corpus data without need for supervision. However, learning the meaning of a (concrete) word also involves establishing a link between the word and its typical visual referents, which is beyond the scope of classic, text-based distributional semantics. Since recently several proposals have been put forward about how to induce multimodal word representations from linguistic and visual contexts, it is natural to ask if this line of work, besides its practical implications, can help us to develop more realistic, grounded models of human word learning within the distributional semantics framework.In my talk, I will report about two studies in which we used multimodal distributional semantics (MDS) to simulate human word learning. In one study, we first measured the ability of subjects to link a nonce word to relevant linguistic and visual associates when prompted only by exposure to minimal corpus evidence about it. We then simulated the same task with an MDS model, finding its behavior remarkably similar to that of subjects. In the second study, we constructed a corpus in which child-directed speech is aligned with real-life pictures of the objects mentioned by care-givers. We then trained our MDS model on these data, and inspected the generaliza...

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“…Our architecture is an encoder-decoder model where our generalized graph attention model and Con-vKB (Nguyen et al, 2018) play the roles of an encoder and decoder, respectively. Moreover, this method can be extended for learning effective embeddings for Textual Entailment Graphs (KOTLERMAN et al, 2015), where global learning has proven effective in the past as shown by (Berant et al, 2015) and (Berant et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

Nathani

Chauhan

Sharma

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

480

225

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The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction). Several recent works suggest that convolutional neural network (CNN) based models generate richer and more expressive feature embeddings and hence also perform well on relation prediction. However, we observe that these KG embeddings treat triples independently and thus fail to cover the complex and hidden information that is inherently implicit in the local neighborhood surrounding a triple. To this effect, our paper proposes a novel attention-based feature embedding that captures both entity and relation features in any given entity's neighborhood. Additionally, we also encapsulate relation clusters and multi-hop relations in our model. Our empirical study offers insights into the efficacy of our attention-based model and we show marked performance gains in comparison to state-of-the-art methods on all datasets.

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Textual entailment graphs

Cited by 19 publications

References 21 publications

A transformation-driven approach for recognizing textual entailment

A transformation-driven approach for recognizing textual entailment

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

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