Articles

Previous research has shown that the mean- ing of many noun-noun compounds N1 N2 can be approximated reasonably well by paraphrasing clauses of the form ‘N2 that . . . N1’, where ‘. . . ’ stands for a verb with or without a preposition. For exam- ple, malaria mosquito …...

SemEval-2 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals. The task was designed to compare different approaches to seman- tic relation classification and to provide a standard testbed for future research. This paper defines the task, describes the train- ing and test data and the …...

We describe the Argument Selection and Coercion task for the SemEval-2010 eval- uation exercise. This task involves char- acterizing the type of compositional oper- ation that exists between a predicate and the arguments it selects. Specifically, the goal is to identify whether the type that a verb selects is satisfied …...

We describe the SemEval-2010 shared task on “Linking Events and Their Partic- ipants in Discourse”. This task is an ex- tension to the classical semantic role label- ing task. While semantic role labeling is traditionally viewed as a sentence-internal task, local semantic argument structures clearly interact with each other in …...

The goal of this task is to evaluate the feasibility of multilingual WSD on a newly developed multilingual lexi- cal sample data set. Participants were asked to automatically determine the contextually appropriate translation of a given English noun in five languages, viz. Dutch, German, Italian, Spanish and French. This paper …...

Tempeval-2 comprises evaluation tasks for time expressions, events and temporal re- lations, the latter of which was split up in four sub tasks, motivated by the notion that smaller subtasks would make both data preparation and temporal relation extrac- tion easier. Manually annotated data were provided for six languages: Chinese, …...

In this paper we describe the SemEval- 2010 Cross-Lingual Lexical Substitution task, where given an English target word in context, participating systems had to find an alternative substitute word or phrase in Spanish. The task is based on the English Lexical Substitution task run at SemEval-2007. In this paper we …...

This paper presents the SemEval-2010 task on Coreference Resolution in Multi- ple Languages. The goal was to evaluate and compare automatic coreference reso- lution systems for six different languages (Catalan, Dutch, English, German, Italian, and Spanish) in four evaluation settings and using four different metrics. Such a rich scenario had …...

This paper presents the HIT_CITYU systems in Semeval-2 Task 18, namely, disambiguat- ing sentiment ambiguous adjectives. The base- line system (HITSZ_CITYU_3) incorporates bi-gram and n-gram collocations of sentiment adjectives, and other context words as features in a one-class Support Vector Machine (SVM) classifier. To enhance the baseline system, col- location …...

The task of extracting the opinion expressed in text is challenging due to different reasons. One of them is that the same word (in particu- lar, adjectives) can have different polarities depending on the context. This paper presents the experiments carried out by the OpAL team for the participation in …...

In this paper, we describe the system we de- veloped for the SemEval-2010 task of disam- biguating sentiment ambiguous adjectives (hereinafter referred to SAA). Our system cre- ated a new word library named SAA-Oriented Library consisting of positive words, negative words, negative words related to SAA, posi- tive words related …...

In this paper, we describe our system which participated in the SemEval 2010 task of disambiguating sentiment ambigu- ous adjectives for Chinese. Our system uses text messages from Twitter, a popu- lar microblogging platform, for building a dataset of emotional texts. Using the built dataset, the system classifies the meaning …...

The document describes the knowledge- based Domain-WSD system using heuris- tic rules (knowledge-base). This HR- WSD system delivered the best perfor- mance (55.9%) among all Chinese sys- tems in SemEval-2010 Task 17: All-words WSD on a specific domain. 1...

This paper describes the UMCC-DLSI system in SemEval-2010 task number 17 (All-words Word Sense Disambiguation on Specific Domain). The main purpose of this work is to evaluate and compare our computational resource of WordNet’s mappings using 3 different methods: Relevant Semantic Tree, Relevant Semantic Tree 2 and an Adaptation of …...

This document describes the prelimi- nary release of the integrated Kyoto sys- tem for specific domain WSD. The sys- tem uses concept miners (Tybots) to ex- tract domain-related terms and produces a domain-related thesaurus, followed by knowledge-based WSD based on word- net graphs (UKB). The resulting system can be applied …...

We describe two approaches for All-words Word Sense Disambiguation on a Spe- cific Domain. The first approach is a knowledge based approach which extracts domain-specific largest connected com- ponents from the Wordnet graph by ex- ploiting the semantic relations between all candidate synsets appearing in a domain- specific untagged corpus. …...

This paper documents the participation of the Research Institute for Artificial Intelligence of the Romanian Academy (RACAI) to the Task 17 – All-words Word Sense Disambiguation on a Specific Domain, of the SemEval-2 com- petition. We describe three unsupervised WSD systems that make extensive use of the Prince- ton WordNet …...

This paper presents an unsupervised sys- tem for all-word domain specific word sense disambiguation task. This system tags target word with the most frequent sense which is estimated using a thesaurus and the word distribution information in the domain. The thesaurus is automati- cally constructed from bilingual parallel corpus using …...

This paper summarizes our participation in task #17 of SemEval–2 (All–words WSD on a specific domain) using a su- pervised class-based Word Sense Disam- biguation system. Basically, we use Sup- port Vector Machines (SVM) as learning algorithm and a set of simple features to build three different models. Each model …...

This paper studies the application of the Web Selectors word sense disambiguation system on a specific domain. The system was primarily applied without any domain tuning, but the incorporation of domain predominant sense information was ex- plored. Results indicated that the system performs relatively the same with domain predominant sense …...