Task #11: English Lexical Sample Task via English-Chinese Parallel Text


Hwee Tou Ng and Yee Seng Chan
National University of Singapore


We propose an English lexical sample task for word sense disambiguation (WSD), where the sense-annotated examples are (semi)-automatically gathered from word-aligned English-Chinese parallel texts. After assigning appropriate Chinese translations to each sense of an English word, the English side of the parallel texts can then serve as the training data, as they are considered to have been disambiguated and "sense-tagged" by the appropriate Chinese translations.

For more details, please refer to the full description for this task and the references given.

Full Description

First, English-Chinese parallel texts are automatically word-aligned. Then the correct Chinese translations corresponding to the different WordNet 1.7.1 senses of an English word are manually selected. Finally, the English half of the parallel texts (the ambiguous English word and its 3-sentence contexts) are used as the training and test material to set up an English lexical sample task.

Since more than one English word sense may be translated by the same Chinese word, two or more English senses s1, s2, ..., sk may be collapsed into one sense in such cases. This gives rise to a lumped sense (coarser-grained) evaluation.

We found from our past work that such an approach of acquiring training examples can yield sense-tagged data of high quality (at least as good as the quality of sense-tagged data for nouns collected in Senseval3 English lexical sample task).

This proposed task is thus similar to the multilingual lexical sample task in Senseval3, except that the training and test examples are collected without manually annotating each individual ambiguous word occurrence.

Datasets and Formats 

We have two tracks for this task, each track using a different corpus. The first corpus is the following English-Chinese parallel corpus available from the Linguistic Data Consortium (LDC):

LDC2005T10 Chinese English News Magazine Parallel Text

It will be used for the evaluation of 50 English words (25 nouns and 25 adjectives). Participants taking part in this track will need to have access to the above LDC corpus in order to access the training and test material in this track. Institutions that are LDC members can obtain the corpus by paying US$150. Institutions that are non-LDC members can obtain the corpus by paying US$2,000.

Since not all interested participants may have access to the above LDC corpus, the second track of this task will make use of English-Chinese documents gathered from the URL pairs given by the STRAND Bilingual Databases. STRAND is a system that acquires document pairs in parallel translation automatically from the Web. We will be using this corpus for the evaluation of 40 English words (20 nouns and 20 adjectives).

Participants in this task can choose to participate in one or both tracks.


The scorer will be the standard Senseval scorer.

Download area

This section will contain evaluation software, useful scripts, complementary materials, baseline systems, etc. but not the datasets proper. The datasets will be available at the main site for download.

Systems and Results

This section will be completed after the competition.


Chan, Yee Seng & Ng, Hwee Tou (2005). Scaling Up Word Sense Disambiguation via Parallel Texts. Proceedings of the 20th 
National Conference on Artificial Intelligence (AAAI 2005). (pp. 1037-1042). Pittsburgh, Pennsylvania, USA.
Ng, Hwee Tou, & Wang, Bin, & Chan, Yee Seng (2003). Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical
Study. Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL-03). 
(pp. 455-462). Sapporo, Japan.
Resnik, Philip & Smith, Noah A (2003). The Web as a Parallel Corpus. Computational Linguistics, Volume 29, Issue 3 (pp. 349-380).