SemEval-2010 Task 3: Cross-Lingual Word Sense Disambiguation
نویسندگان
چکیده
We propose a multilingual unsupervised Word Sense Disambiguation (WSD) task for a sample of English nouns. Instead of providing manually sensetagged examples for each sense of a polysemous noun, our sense inventory is built up on the basis of the Europarl parallel corpus. The multilingual setup involves the translations of a given English polysemous noun in five supported languages, viz. Dutch, French, German, Spanish and Italian. The task targets the following goals: (a) the manual creation of a multilingual sense inventory for a lexical sample of English nouns and (b) the evaluation of systems on their ability to disambiguate new occurrences of the selected polysemous nouns. For the creation of the hand-tagged gold standard, all translations of a given polysemous English noun are retrieved in the five languages and clustered by meaning. Systems can participate in 5 bilingual evaluation subtasks (English Dutch, English German, etc.) and in a multilingual subtask covering all language pairs. As WSD from cross-lingual evidence is gaining popularity, we believe it is important to create a multilingual gold standard and run cross-lingual WSD benchmark tests.
منابع مشابه
UHD: Cross-Lingual Word Sense Disambiguation Using Multilingual Co-Occurrence Graphs
We describe the University of Heidelberg (UHD) system for the Cross-Lingual Word Sense Disambiguation SemEval-2010 task (CL-WSD). The system performs CLWSD by applying graph algorithms previously developed for monolingual Word Sense Disambiguation to multilingual cooccurrence graphs. UHD has participated in the BEST and out-of-five (OOF) evaluations and ranked among the most competitive systems...
متن کاملUvT-WSD1: A Cross-Lingual Word Sense Disambiguation System
This paper describes the Cross-Lingual Word Sense Disambiguation system UvTWSD1, developed at Tilburg University, for participation in two SemEval-2 tasks: the Cross-Lingual Word Sense Disambiguation task and the Cross-Lingual Lexical Substitution task. The UvT-WSD1 system makes use of k-nearest neighbour classifiers, in the form of single-word experts for each target word to be disambiguated. ...
متن کاملLIMSI : Cross-lingual Word Sense Disambiguation using Translation Sense Clustering
We describe the LIMSI system for the SemEval-2013 Cross-lingual Word Sense Disambiguation (CLWSD) task. Word senses are represented by means of translation clusters in different languages built by a cross-lingual Word Sense Induction (WSI) method. Our CLWSD classifier exploits the WSI output for selecting appropriate translations for target words in context. We present the design of the system ...
متن کاملStandard Test Collection for English-Persian Cross-Lingual Word Sense Disambiguation
In this paper, we address the shortage of evaluation benchmarks on Persian (Farsi) language by creating and making available a new benchmark for English to Persian Cross Lingual Word Sense Disambiguation (CL-WSD). In creating the benchmark, we follow the format of the SemEval 2013 CL-WSD task, such that the introduced tools of the task can also be applied on the benchmark. In fact, the new benc...
متن کاملNRC: A Machine Translation Approach to Cross-Lingual Word Sense Disambiguation (SemEval-2013 Task 10)
This paper describes the NRC submission to the Spanish Cross-Lingual Word Sense Disambiguation task at SemEval-2013. Since this word sense disambiguation task uses Spanish translations of English words as gold annotation, it can be cast as a machine translation problem. We therefore submitted the output of a standard phrase-based system as a baseline, and investigated ways to improve its sense ...
متن کاملFCC: Modeling Probabilities with GIZA++ for Task 2 and 3 of SemEval-2
In this paper we present a naı̈ve approach to tackle the problem of cross-lingual WSD and cross-lingual lexical substitution 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 EUROPARL parallel corpus, in order to calculate the probability of a source word to be translated to a target word ...
متن کامل