Articles

This paper describes the design of a sys- tem for extracting keyphrases from a sin- gle document The principle of the algo- rithm is to cluster sentences of the doc- uments in order to highlight parts of text that are semantically related. The clusters of sentences, that reflect the themes …...

We describe our method for extracting keyphrases from scientific articles which we participate in the shared task of SemEval-2 Evaluation Exercise. Even though general-purpose term extractors along with linguistically-motivated analy- sis allow us to extract elaborated morpho- syntactic variation forms of terms, a na¨ıve statistic approach proposed in this paper …...

A central issue for making the content of a scientific document quickly acces- sible to a potential reader is the extrac- tion of keyphrases, which capture the main topic of the document. Keyphrases can be extracted automatically by generating a list of keyphrase candidates, ranking these candidates, and selecting the …...

The DERI UNLP team participated in the SemEval 2010 Task #5 with an unsuper- vised system that automatically extracts keyphrases from scientific articles. Our approach does not only consider a general description of a term to select keyphrase candidates but also context information in the form of “skill types”. Even …...

We describe the University of Heidelberg (UHD) system for the Cross-Lingual Word Sense Disambiguation SemEval-2010 task (CL-WSD). The system performs CL- WSD by applying graph algorithms pre- viously developed for monolingual Word Sense Disambiguation to multilingual co- occurrence graphs. UHD has participated in the BEST and out-of-five (OOF) eval- uations …...

We report here our work on English French Cross-lingual Word Sense Disam- biguation where the task is to find the best French translation for a target English word depending on the context in which it is used. Our approach relies on identifying the nearest neighbors of the test sentence from …...

In this paper, we present a word sense disambiguation (WSD) based system for multilingual lexical substitution. Our method depends on having a WSD system for English and an automatic word align- ment method. Crucially the approach re- lies on having parallel corpora. For Task 2 (Sinha et al., 2009) we …...

We present two systems that select the most appropriate Spanish substitutes for a marked word in an English test sen- tence. These systems were official en- tries to the SemEval-2010 Cross-Lingual Lexical Substitution task. The first sys- tem, SWAT-E, finds Spanish substitutions by first finding English substitutions in the English …...

In this paper we present a na¨ıve approach to tackle the problem of cross-lingual WSD and cross-lingual lexical substitu- tion which correspond to the Task #2 and #3 of the SemEval-2 competition. We used a bilingual statistical dictionary, which is calculated with Giza++ by using the EU- ROPARL parallel corpus, …...

This paper provides a description of the Hong Kong Polytechnic University (PolyU) System that participated in the task #5 of SemEval-2, i.e., the Automatic Keyphrase Extraction from Scientific Articles task. We followed a novel framework to develop our keyphrase extraction system, motivated by differentiating the roles of the words in …...

BART (Versley et al., 2008) is a highly mod- ular toolkit for coreference resolution that supports state-of-the-art statistical approaches and enables efficient feature engineering. For the SemEval task 1 on Coreference Resolu- tion, BART runs have been submitted for Ger- man, English, and Italian. BART relies on a maximum entropy-based …...

Corry is a system for coreference resolution in English. It supports both local (Soon et al. (2001)-style) and global (Integer Linear Programming, Denis and Baldridge (2007)- style) models of coreference. Corry relies on a rich linguistically motivated feature set, which has, however, been manually reduced to 64 features for efficiency …...

We present UBIU, a language indepen- dent system for detecting full coreference chains, composed of named entities, pro- nouns, and full noun phrases which makes use of memory based learning and a fea- ture model following Rahman and Ng (2009). UBIU is evaluated on the task “Coreference Resolution in Multiple …...

We describe two systems participating in Semeval-2010’s Cross-Lingual Lexical Substitution task: USPwlv and WLVusp. Both systems are based on two main com- ponents: (i) a dictionary to provide a num- ber of possible translations for each source word, and (ii) a contextual model to select the best translation according to …...

This paper describes the participation of RelaxCor in the Semeval-2010 task number 1: “Coreference Resolution in Multiple Languages“. RelaxCor is a constraint-based graph partitioning ap- proach to coreference resolution solved by relaxation labeling. The approach com- bines the strengths of groupwise classifiers and chain formation methods in one global method. …...

Sentiment ambiguous adjectives cause major difficulties for existing algorithms of sentiment analysis. We present an evaluation task designed to provide a framework for comparing different approaches in this problem. We define the task, describe the data creation, list the participating systems and discuss their results. There are 8 teams and …...

This paper presents SUCRE, a new soft- ware tool for coreference resolution and its feature engineering. It is able to sep- arately do noun, pronoun and full coref- erence resolution. SUCRE introduces a new approach to the feature engineering of coreference resolution based on a rela- tional database model and …...

An overview of the SemEval-2 Japanese WSD task is presented. It is a lexical sample task, and word senses are defined according to a Japanese dictionary, the Iwanami Kokugo Jiten. This dictionary and a training corpus were distributed to participants. The number of target words was 50, with 22 nouns, …...

This paper presents the description and evaluation framework of SemEval-2010 Word Sense Induction & Disambiguation task, as well as the evaluation results of 26 participating systems. In this task, partici- pants were required to induce the senses of 100 target words using a training set, and then disambiguate unseen instances …...

Parser Evaluation using Textual Entail- ments (PETE) is a shared task in the SemEval-2010 Evaluation Exercises on Semantic Evaluation. The task involves recognizing textual entailments based on syntactic information alone. PETE intro- duces a new parser evaluation scheme that is formalism independent, less prone to annotation error, and focused on …...