SEMEVAL

This paper describes the specifications and results of UMCC_DLSI system, which participated in the first Semantic Textual Similarity task (STS) of SemEval-2012. Our supervised system uses different kinds of semantic and lexical features to train ...

We describe the systems submitted by SRI International and the University of the Basque Country for the Semantic Textual Similarity (STS) SemEval-2012 task. Our systems focused on using a simple set of features, featuring a mix of semantic similarity ...

This paper describes the participation of FBK in the Semantic Textual Similarity (STS) task organized within Semeval 2012. Our approach explores lexical, syntactic and semantic machine translation evaluation metrics combined with distributional and ...

In this paper we describe the three approaches we submitted to the Semantic Textual Similarity task of SemEval 2012. The first approach considers to calculate the semantic similarity by using the Jaccard coefficient with term expansion using synonyms. ...

This paper presents the systems that we participated with in the Semantic Text Similarity task at SEMEVAL 2012. Based on prior research in semantic similarity and relatedness, we combine various methods in a machine learning framework. The three ...

In this paper, we describe our system submitted for the semantic textual similarity (STS) task at SemEval 2012. We implemented two approaches to calculate the degree of similarity between two sentences. First approach combines corpus-based semantic ...

This paper describes Stanford University's submission to SemEval 2012 Semantic Textual Similarity (STS) shared evaluation task. Our proposed metric computes probabilistic edit distance as predictions of semantic similarity. We learn weighted edit ...

This paper describes the University of Sheffield's submission to SemEval-2012 Task 6: Semantic Text Similarity. Two approaches were developed. The first is an unsupervised technique based on the widely used vector space model and information from ...

Sentences that are syntactically quite different can often have similar or same meaning. The SemEval 2012 task of Semantic Textual Similarity aims at finding the semantic similarity between two sentences. The semantic representation of Universal ...

In this paper we report the results obtained in the Semantic Textual Similarity (STS) task, with a system primarily developed for textual entailment. Our results are quite promising, getting a run ranked 39 in the official results with overall Pearson, ...

The UOW submissions to the Semantic Textual Similarity task at SemEval-2012 use a supervised machine learning algorithm along with features based on lexical, syntactic and semantic similarity metrics to predict the semantic equivalence between a pair of ...

We present the Penn system for SemEval-2012 Task 6, computing the degree of semantic equivalence between two sentences. We explore the contributions of different vector models for computing sentence and word similarity: Collobert and Weston embeddings ...

This paper presents a novel approach for building adaptive similarity functions based on cardinality using machine learning. Unlike current approaches that build feature sets using similarity scores, we have developed these feature sets with the ...

This article presents the experiments carried out at Jadavpur University as part of the participation in Cross-lingual Textual Entailment for Content Synchronization (CLTE) of task 8 @ Semantic Evaluation Exercises (SemEval-2012). The work explores ...

This paper presents CELI's participation in the SemEval Cross-lingual Textual Entailment for Content Synchronization task.

This paper overviews FBK's participation in the Cross-Lingual Textual Entailment for Content Synchronization task organized within SemEval-2012. Our participation is characterized by using cross-lingual matching features extracted from lexical and ...

In this paper we present a report of the two different runs submitted to the task 8 of Semeval 2012 for the evaluation of Cross-lingual Textual Entailment in the framework of Content Synchronization. Both approaches are based on textual similarity, and ...

There are relatively few entailment heuristics that exploit the directional nature of the entailment relation. Cross-Lingual Text Entailment (CLTE), besides introducing the extra dimension of cross-linguality, also requires to determine the exact ...

In this paper, we present our system description in task of Cross-lingual Textual Entailment. The goal of this task is to detect entailment relations between two sentences written in different languages. To accomplish this goal, we first translate ...

This paper describes our participation in the task denominated Cross-Lingual Textual Entailment (CLTE) for content synchronization. We represent an approach to CLTE using machine translation to tackle the problem of multilinguality. Our system resides ...