Distributional semantic models for detection of textual entailment
نویسندگان
چکیده
We present our experiments on integrating and evaluating distributional semantics with the recognising textual entailment task (RTE). We consider entailment as semantic similarity between text and hypothesis coupled with additional heuristic, which can be either selecting the top scoring hypothesis or a pre-defined threshold. We show that a distributional model is particularly good at detecting entailment related to “world knowledge”, and that aligning the hypothesis with the text improves detection of lexical
منابع مشابه
SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment
This paper presents the task on the evaluation of Compositional Distributional Semantics Models on full sentences organized for the first time within SemEval2014. Participation was open to systems based on any approach. Systems were presented with pairs of sentences and were evaluated on their ability to predict human judgments on (i) semantic relatedness and (ii) entailment. The task attracted...
متن کاملUIO-Lien: Entailment Recognition using Minimal Recursion Semantics
In this paper we present our participation in the Semeval 2014 task “Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment”. Our results demonstrate that using generic tools for semantic analysis is a viable option for a system that recognizes textual entailment. The invested effort in developing such tools allows us to ...
متن کاملBUAP: Evaluating Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment
The results obtained by the BUAP team at Task 1 of SemEval 2014 are presented in this paper. The run submitted is a supervised version based on two classification models: 1) We used logistic regression for determining the semantic relatedness between a pair of sentences, and 2) We employed support vector machines for identifying textual entailment degree between the two sentences. The behaviour...
متن کاملIdentifying Lexical Relationships and Entailments with Distributional Semantics
As the field of Natural Language Processing has developed, research has progressed on ambitious semantic tasks like Recognizing Textual Entailment (RTE). Systems that approach these tasks may perform sophisticated inference between sentences, but often depend heavily on lexical resources like WordNet to provide critical information about relationships and entailments between lexical items. Howe...
متن کاملVisual Denotations for Recognizing Textual Entailment
In the logic approach to Recognizing Textual Entailment, identifying phrase-tophrase semantic relations is still an unsolved problem. Resources such as the Paraphrase Database offer limited coverage despite their large size whereas unsupervised distributional models of meaning often fail to recognize phrasal entailments. We propose to map phrases to their visual denotations and compare their me...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2016