Articles

Holding non-co-located conversations while driving is dangerous (Horrey and Wickens, 2006; Strayer et al., 2006), much more so than conversations with physically present, “situated” interlocutors (Drews et al., 2004). In-car dialogue systems typically resemble non-co-located conversations more, and share their negative impact (Strayer et al., 2013). We implemented and tested …...

In this paper we present our systems for cal- culating the degree of semantic similarity be- tween two texts that we submitted to the Se- mantic Textual Similarity task at SemEval- 2013. Our systems predict similarity using a regression over features based on the fol- lowing sources of information: string …...

This paper presents three methods to evaluate the Semantic Textual Similarity (STS). The first two methods do not require labeled train- ing data; instead, they automatically extract semantic knowledge in the form of word asso- ciations from a given reference corpus. Two kinds of word associations are considered: co- occurrence …...

This paper describes the system that was sub- mitted in the *SEM 2013 Semantic Textual Similarity shared task. The task aims to find the similarity score between a pair of sen- tences. We describe a Universal Network- ing Language (UNL) based semantic extrac- tion system for measuring the semantic simi- …...

CLaC-CORE, an exhaustive feature combina- tion system ranked 4th among 34 teams in the Semantic Textual Similarity shared task STS 2013. Using a core set of 11 lexical features of the most basic kind, it uses a support vector regressor which uses a combination of these lexical features to train …...

Soft cardinality has been shown to be a very strong text-overlapping baseline for the task of measuring semantic textual similarity (STS), obtaining 3rd place in SemEval-2012. At *SEM-2013 shared task, beside the plain text- overlapping approach, we tested within soft cardinality two distributional word-similarity functions derived from the ukWack corpus. …...

We present in this paper the systems we participated with in the Semantic Textual Similarity task at SEM 2013. The Semantic Textual Similarity Core task (STS) computes the degree of semantic equivalence between two sentences where the participant systems will be compared to the manual scores, which range from 5 …...

This paper describes our system entered for the *SEM 2013 shared task on Semantic Textual Similarity (STS). We focus on the core task of predicting the semantic textual similarity of sentence pairs. The current system utilizes machine learn- ing techniques trained on semantic similarity ratings from the *SEM 2012 shared …...

This paper describes the system used by the LIPN team in the Semantic Textual Similarity task at *SEM 2013. It uses a support vector re- gression model, combining different text sim- ilarity measures that constitute the features. These measures include simple distances like Levenshtein edit distance, cosine, Named En- tities …...

This paper describes the UNIBA participation in the Semantic Textual Similarity (STS) core task 2013. We exploited three different sys- tems for computing the similarity between two texts. A system is used as baseline, which rep- resents the best model emerged from our pre- vious participation in STS 2012. Such …...

In this year’s Semantic Textual Similarity evaluation, we explore the contribution of models that provide soft similarity scores across spans of multiple words, over the pre- vious year’s system. To this end, we ex- plored the use of neural probabilistic language models and a TF-IDF weighted variant of Ex- plicit …...

The Semantic Textual Similarity (STS) task examines semantic similarity at a sentence- level. We explored three representations of semantics (implicit or explicit): named enti- ties, semantic vectors, and structured vectorial semantics. From a DKPro baseline, we also performed feature selection and used source- specific linear regression models to combine our …...

In this paper we discuss our participation to the 2013 Semeval Semantic Textual Similarity task. Our core features include (i) a set of met- rics borrowed from automatic machine trans- lation, originally intended to evaluate auto- matic against reference translations and (ii) an instance of explicit semantic analysis, built upon …...

We approach the typed-similarity task using a range of heuristics that rely on information from the appropriate metadata fields for each type of similarity. In addition we train a linear regressor for each type of similarity. The re- sults indicate that the linear regression is key for good performance. Our …...

This paper reports our submissions to the Semantic Textual Similarity (STS) task in ∗SEM Shared Task 2013. We submitted three Support Vector Regression (SVR) systems in core task, using 6 types of similarity mea- sures, i.e., string similarity, number similar- ity, knowledge-based similarity, corpus-based similarity, syntactic dependency similarity and machine …...

This paper describes the specifications and results of UMCC_DLSI system, which participated in the Semantic Textual Similarity task (STS) of SemEval-2013. Our supervised system uses different types of lexical and semantic features to train a Bagging classifier used to decide the correct option. Related to the different features we can …...

This paper deals with knowledge-based text processing which aims at an intuitive notion of textual similarity. Entities and relations rel- evant for a particular domain are identified and disambiguated by means of semi-supervised machine learning techniques and resulting an- notations are applied for computing typed- similarity of individual texts. The …...

This paper describes our submission for the *SEM shared task of Semantic Textual Sim- ilarity. We estimate the semantic similarity between two sentences using regression mod- els with features: 1) n-gram hit rates (lexical matches) between sentences, 2) lexical seman- tic similarity between non-matching words, 3) string similarity metrics, 4) …...

This paper describes a system for automat- ically measuring the semantic similarity be- tween two texts, which was the aim of the 2013 Semantic Textual Similarity (STS) task (Agirre et al., 2013). For the 2012 STS task, Heilman and Madnani (2012) submitted the PERP system, which performed competitively in relation …...

The Semantic Textual Similarity (STS) task aims to exam the degree of semantic equivalence between sentences (Agirre et al., 2012). This paper presents the work of the Hong Kong Polytechnic University (PolyUCOMP) team which has participated in the STS core and typed tasks of SemEval- 2013. For the STS core …...