The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity

نویسندگان

  • Johannes Bjerva
  • Johan Bos
  • Rob van der Goot
  • Malvina Nissim
چکیده

Shared Task 1 of SemEval-2014 comprised two subtasks on the same dataset of sentence pairs: recognizing textual entailment and determining textual similarity. We used an existing system based on formal semantics and logical inference to participate in the first subtask, reaching an accuracy of 82%, ranking in the top 5 of more than twenty participating systems. For determining semantic similarity we took a supervised approach using a variety of features, the majority of which was produced by our system for recognizing textual entailment. In this subtask our system achieved a mean squared error of 0.322, the best of all participating systems.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Recognizing Textual Entailment Using Description Logic and Semantic Relatedness

Recognizing Textual Entailment using Description Logic and Semantic Relatedness Reda Siblini, Ph.D. Concordia University, 2014 Textual entailment (TE) is a relation that holds between two pieces of text where one reading the first piece can conclude that the second is most likely true. Accurate approaches for textual entailment can be beneficial to various natural language processing (NLP) appl...

متن کامل

Measuring Semantic Similarity in Short Texts through Greedy Pairing and Word Semantics

We propose in this paper a greedy method to the problem of measuring semantic similarity between short texts. Our method is based on the principle of compositionality which states that the overall meaning of a sentence can be captured by summing up the meaning of its parts, i.e. the meanings of words in our case. Based on this principle, we extend wordto-word semantic similarity metrics to quan...

متن کامل

Learning Parse-Free Event-Based Features for Textual Entailment Recognition

We propose new parse-free event-based features to be used in conjunction with lexical, syntactic, and semantic features of texts and hypotheses for Machine Learning-based Recognizing Textual Entailment. Our new similarity features are extracted without using shallow semantic parsers, but still lexical and compositional semantics are not left out. Our experimental results demonstrate that these ...

متن کامل

Expanded Dependency Structure based Textual Entailment Recognition System of NTTDATA for NTCIR10-RITE2

This paper describes NTT DATA’s recognizing textual entailment(RTE) systems for NTCIR10 RITE2. We participate in four Japanese tasks, BC Subtask, Unit Test, Exam BC and Exam Search[5]. Our approach uses a ratio with the same semantic relations between words. It is necessary to recognize two semantic viewpoints, which are the semantic relation and the meaning between words in a sentence, in orde...

متن کامل

Visual 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 کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014