Learning entailment rules for unary templates

Szpektor, Idan; Dagan, Ido

doi:10.3115/1599081.1599188

Cited by 57 publications

(72 citation statements)

References 20 publications

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“…Balprec is a measure created by Szpektor and Dagan (2008). They proposed combining WeedsPrec together with the Lin measure by taking their geometric average.…”

Section: Similarity Measuresmentioning

confidence: 99%

Looking for Hyponyms in Vector Space

Rei

Briscoe

2014

Proceedings of the Eighteenth Conference on Computational Natural Language Learning

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The task of detecting and generating hyponyms is at the core of semantic understanding of language, and has numerous practical applications. We investigate how neural network embeddings perform on this task, compared to dependency-based vector space models, and evaluate a range of similarity measures on hyponym generation. A new asymmetric similarity measure and a combination approach are described, both of which significantly improve precision. We release three new datasets of lexical vector representations trained on the BNC and our evaluation dataset for hyponym generation.

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“…Balprec is a measure created by Szpektor and Dagan (2008). They proposed combining WeedsPrec together with the Lin measure by taking their geometric average.…”

Section: Similarity Measuresmentioning

confidence: 99%

Looking for Hyponyms in Vector Space

Rei

Briscoe

2014

Proceedings of the Eighteenth Conference on Computational Natural Language Learning

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“…Given context vectors, Lin and Pantel (2001) used a symmetric similarity metric (Lin, 1998) to find candidate paraphrases. We build dependency context vectors for each word in our data and compute both symmetric as well as more recently proposed asymmetric similarity measures (Weeds et al, 2004;Szpektor and Dagan, 2008;Clarke, 2009), which are potentially better suited for identifying A paraphrases. Table 3 gives a comparison of the pairs which are considered "most similar" according to several of these metrics.…”

Section: Monolingual Featuresmentioning

confidence: 99%

Adding Semantics to Data-Driven Paraphrasing

Pavlick¹,

Bos²,

Nissim³

et al. 2015

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere

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We add an interpretable semantics to the paraphrase database (PPDB). To date, the relationship between phrase pairs in the database has been weakly defined as approximately equivalent. We show that these pairs represent a variety of relations, including directed entailment (little girl/girl) and exclusion (nobody/someone). We automatically assign semantic entailment relations to entries in PPDB using features derived from past work on discovering inference rules from text and semantic taxonomy induction. We demonstrate that our model assigns these relations with high accuracy. In a downstream RTE task, our labels rival relations from WordNet and improve the coverage of a proof-based RTE system by 17%.

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“…Therefore, they calculate the synonymy between two relations by comparing the arguments with which they occur. Several methods have been proposed [30,31,52,49,54], which differ in the representation of the predicates, the extracted features and the function used to compute the similarity of the feature vectors. To be effective, distributional metrics must rely on some weighting scheme over the relation features.…”

Section: Synonym Resolutionmentioning

confidence: 99%

Exploiting semantic annotations for open information extraction: an experience in the biomedical domain

Nebot

Berlanga

2012

Knowl Inf Syst

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Abstract.The increasing amount of unstructured text published on the Web is demanding new tools and methods to automatically process and extract relevant information. Traditional information extraction has focused on harvesting domain-specific, pre-specified relations, which usually requires manual labor and heavy machinery. Especially in the biomedical domain the main efforts have been directed towards the recognition of welldefined entities such as genes or proteins, which constitutes the basis for extracting the relations between the recognized entities. The intrinsic features and scale of the Web demand new approaches able to cope with the diversity of documents, where the number of relations is unbounded and not known in advance. This paper presents a scalable method for the extraction of domain-independent relations from text that exploits the knowledge in the semantic annotations. The method is not geared to any specific domain (e.g., protein-protein interactions, drug-drug interactions, etc.) and does not require any manual input or deep processing. Moreover, the method uses the extracted relations to compute groups of abstract semantic relations characterized by their signature types and synonymous relation strings. This constitutes a valuable source of knowledge when constructing formal knowledge bases, as we enable seamless integration of the extracted relations with the available knowledge resources through the process of semantic annotation. The proposed approach has successfully been applied to a large text collection in the biomedical domain and the results are very encouraging.

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Learning entailment rules for unary templates

Cited by 57 publications

References 20 publications

Looking for Hyponyms in Vector Space

Looking for Hyponyms in Vector Space

Adding Semantics to Data-Driven Paraphrasing

Exploiting semantic annotations for open information extraction: an experience in the biomedical domain

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