Articles

This paper describes Stanford University’s submission to SemEval 2012 Semantic Tex- tual Similarity (STS) shared evaluation task. Our proposed metric computes probabilistic edit distance as predictions of semantic sim- ilarity. We learn weighted edit distance in a probabilistic finite state machine (pFSM) model, where state transitions correspond to edit operations. …...

In this paper, we describe our system submit- ted for the semantic textual similarity (STS) task at SemEval 2012. We implemented two approaches to calculate the degree of simi- larity between two sentences. First approach combines corpus-based semantic relatedness measure over the whole sentence with the knowledge-based semantic similarity scores …...

This paper presents the systems that we par- ticipated with in the Semantic Text Similar- ity task at SEMEVAL 2012. Based on prior research in semantic similarity and related- ness, we combine various methods in a ma- chine learning framework. The three varia- tions submitted during the task evaluation pe- …...

In this paper we describe the three approaches we submitted to the Semantic Textual Similar- ity task of SemEval 2012. The first approach considers to calculate the semantic similar- ity by using the Jaccard coefficient with term expansion using synonyms. The second ap- proach uses the semantic similarity reported by …...

This paper describes the participation of FBK in the Semantic Textual Similarity (STS) task organized within Semeval 2012. Our ap- proach explores lexical, syntactic and se- mantic machine translation evaluation metrics combined with distributional and knowledge- based word similarity metrics. Our best model achieves 60.77% correlation with hu- man judgements …...

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 classifiers and it uses a voting process to select the correct option. Related to the …...

We describe the systems submitted by SRI In- ternational and the University of the Basque Country for the Semantic Textual Similarity (STS) SemEval-2012 task. Our systems fo- cused on using a simple set of features, fea- turing a mix of semantic similarity resources, lexical match heuristics, and part of speech …...

This paper describes our system for the Se- meval 2012 Sentence Textual Similarity task. The system is based on a combination of few simple vector space-based methods for word meaning similarity. Evaluation results show that a simple combination of these unsuper- vised data-driven methods can be quite suc- cessful. The …...

This paper presents the UNITOR system that participated to the SemEval 2012 Task 6: Se- mantic Textual Similarity (STS). The task is here modeled as a Support Vector (SV) regres- sion problem, where a similarity scoring func- tion between text pairs is acquired from exam- ples. The semantic relatedness between …...

The Semantic Textual Similarity (STS) shared task (Agirre et al., 2012) computes the degree of semantic equivalence between two sen- tences.1 We show that a simple unsupervised latent semantics based approach, Weighted Textual Matrix Factorization that only exploits bag-of-words features, can outperform most systems for this task. The key to …...

The paper aims to come up with a sys- tem that examines the degree of semantic equivalence between two sentences. At the core of the paper is the attempt to grade the similarity of two sentences by find- ing the maximal weighted bipartite match between the tokens of the two …...

This paper briefly reports our submissions to the Semantic Textual Similarity (STS) task in the SemEval 2012 (Task 6). We first use knowledge-based methods to compute word semantic similarity as well as Word Sense Dis- ambiguation (WSD). We also consider word order similarity from the structure of the sen- tence. …...

We estimate the semantic similarity between two sentences using regression models with features: 1) n-gram hit rates (lexical matches) between sentences, 2) lexical semantic sim- ilarity between non-matching words, and 3) sentence length. Lexical semantic similarity is computed via co-occurrence counts on a cor- pus harvested from the web using …...

This article presents the experiments car- ried out at Jadavpur University as part of the participation in Semantic Textual Si- milarity (STS) of Task 6 @ Semantic Evaluation Exercises (SemEval-2012). Task-6 of SemEval- 2012 focused on se- mantic relations of text pair. Task-6 pro- vides five different text pair files …...

In this paper we present our systems for the STS task. Our systems are all based on a simple process of identifying the components that correspond between two sentences. Cur- rently we use words (that is word forms), lem- mas, distributional similar words and gram- matical relations identified with a …...

This paper describes the participation of the IRIT team to SemEval 2012 Task 6 (Seman- tic Textual Similarity). The method used con- sists of a n-gram based comparison method combined with a conceptual similarity mea- sure that uses WordNet to calculate the sim- ilarity between a pair of concepts. 1...

We propose a semantic similarity learning method based on Random Indexing (RI) and ranking with boosting. Unlike classical RI, we use only those context vector features that are informative for the semantics modeled. De- spite ignoring text preprocessing and dispens- ing with semantic resources, the approach was ranked as high …...

This paper describes investigations into using syntactic chunk information as the basis for determining the similarity of candidate texts at the semantic level. Two approaches were con- sidered. The first was a corpus-based method that extracted lexical and semantic features from pairs of chunks from each sentence that were associated …...

In this paper, we describe the system architec- ture used in the Semantic Textual Similarity (STS) task 6 pilot challenge. The goal of this challenge is to accurately identify five levels of semantic similarity between two sentences: equivalent, mostly equivalent, roughly equiva- lent, not equivalent but sharing the same topic …...

Many problems in natural language process- ing can be viewed as variations of the task of measuring the semantic textual similarity be- tween short texts. However, many systems that address these tasks focus on a single task and may or may not generalize well. In this work, we extend an …...