Articles

This paper presents the work of the Hong Kong Polytechnic University (PolyUCOMP) team which has participated in the Semantic Textual Similarity task of SemEval-2012. The PolyUCOMP system combines semantic vec- tors with skip bigrams to determine sentence similarity. The semantic vector is used to compute similarities between sentence pairs using …...

In this paper, we introduce our work on SemEval-2012 task 5: Chinese Semantic De- pendency Parsing. Our system is based on MSTParser and two effective methods are proposed: splitting sentence by punctuations and extracting last character of word as lemma. The experiments show that, with a combina- tion of the …...

The goal of semantic dependency parsing is to build dependency structure and label seman- tic relation between a head and its modifier. To attain this goal, we concentrate on obtain- ing better dependency structure to predict bet- ter semantic relations, and propose a method to combine the results of three …...

We describe a system proposed for measuring the degree of relational similarity beetwen a pair of words at the Task #2 of Semeval 2012. The approach presented is based on a vec- torial representation using the following fea- tures: i) the context surrounding the words with a windows size = …...

This paper describes the Duluth systems that participated in Task 2 of SemEval–2012. These systems were unsupervised and relied on variations of the Gloss Vector measure found in the freely available software pack- age WordNet::Similarity. This method was moderately successful for the Class-Inclusion, Similar, Contrast, and Non-Attribute cate- gories of …...

This paper presents three systems that took part in the lexical simplification task at SE- MEVAL 2012. Speculating on what the con- cept of simplicity might mean for a word, the systems apply different approaches to rank the given candidate lists. One of the systems performs second-best (statistically significant) and …...

This paper presents the systems we developed while participating in the first task (English Lexical Simplification) of SemEval 2012. Our first system relies on n-grams frequencies computed from the Simple English Wikipedia version, ranking each substitution term by de- creasing frequency of use. We experimented with several other systems, based …...

This paper describes SimpLex,1 a Lexical Simplification system that participated in the English Lexical Simplification shared task at SemEval-2012. It operates on the basis of a linear weighted ranking function composed of context sensitive and psycholinguistic fea- tures. The system outperforms a very strong baseline, and ranked first on the …...

In this paper, we describe the system we sub- mitted to the SemEval-2012 Lexical Simplifi- cation Task. Our system (mmSystem) com- bines word frequency with decompositional semantics criteria based on syntactic structure in order to rank candidate substitutes of lexical forms of arbitrary syntactic complexity (one- word, multi-word, etc.) in …...

This paper describes a new method for cross- lingual textual entailment (CLTE) detection based on machine translation (MT). We use sub-segment translations from different MT systems available online as a source of cross- lingual knowledge. In this work we describe and evaluate different features derived from these sub-segment translations, which …...

We describe the Heidelberg University system for the Cross-lingual Textual Entailment task at SemEval-2012. The system relies on fea- tures extracted with statistical machine trans- lation methods and tools, combining mono- lingual and cross-lingual word alignments as well as standard textual entailment dis- tance and bag-of-words features in a statisti- …...

This paper describes the participation of UNED NLP group in the SEMEVAL 2012 Se- mantic Textual Similarity task. Our contribu- tion consists of an unsupervised method, Het- erogeneity Based Ranking (HBR), to combine similarity measures. Our runs focus on com- bining standard similarity measures for Ma- chine Translation. The Pearson …...

We present an approach for the construction of text similarity functions using a parameterized resem- blance coefficient in combination with a softened cardinality function called soft cardinality. Our ap- proach provides a consistent and recursive model, varying levels of granularity from sentences to char- acters. Therefore, our model was used …...

This paper describes the two systems for determining the semantic similarity of short texts submitted to the SemEval 2012 Task 6. Most of the research on semantic similarity of textual content focuses on large documents. However, a fair amount of information is con- densed into short text snippets such as …...

This paper presents our system participated on SemEval-2012 task: Chinese Semantic De- pendency Parsing. Our system extends the second-order MST model by adding two third-order features. The two third-order fea- tures are grand-sibling and tri-sibling. In the decoding phase, we keep the k best results for each span. After using …...

We present the UKP system which performed best in the Semantic Textual Similarity (STS) task at SemEval-2012 in two out of three met- rics. It uses a simple log-linear regression model, trained on the training data, to combine multiple text similarity measures of varying complexity. These range from simple char- …...

This document describes three systems calcu- lating semantic similarity between two Chi- nese words. One is based on Machine Readable Dictionaries and the others utilize both MRDs and Corpus. These systems are performed on SemEval-2012 Task 4: Evaluat- ing Chinese Word Similarity. 1...

We present a joint approach for recognizing spatial roles in SemEval-2012 Task 3. Can- didate spatial relations, in the form of triples, are heuristically extracted from sentences with high recall. The joint classification of spatial roles is then cast as a binary classification over the candidates. This joint approach allows …...

In this paper we present our approach for assigning degrees of relational similarity to pairs of words in the SemEval-2012 Task 2. To measure relational similarity we employed lexical patterns that can match against word pairs within a large corpus of 12 million docu- ments. Patterns are weighted by obtaining …...

This paper presents the first round of the task on Cross-lingual Textual Entailment for Content Synchronization, organized within SemEval-2012. The task was designed to pro- mote research on semantic inference over texts written in different languages, targeting at the same time a real application scenario. Par- ticipants were presented with …...