TreeMatch: A Fully Unsupervised WSD System Using Dependency Knowledge on a Specific Domain

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 396–401, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics TreeMatch: A Fully Unsupervised WSD System Using Dependency Knowledge on a Specific Domain Andrew Tran Chris Bowes David Brown Ping Chen University of Houston-Downtown Max Choly Wei Ding University of Massachusetts-Boston Abstract Word sense disambiguation (WSD) is one of the main challenges in Computational Linguistics. TreeMatch is a WSD system originally developed using data from SemEval 2007 Task 7 (Coarse-grained English All- words Task) that has been adapted for use in SemEval 2010 Task 17 (All-words Word Sense Disambiguation on a Specific Domain). The system is based on a fully unsupervised method using dependency knowledge drawn from a domain specific knowledge base that was built for this task. When evaluated on the task, the system precision performs above the First Sense Baseline. 1 Introduction There are many words within natural languages that can have multiple meanings or senses depending on its usage. These words are called homographs. Word sense disambiguation is the process of determining which sense of a homograph is correct in a given context. Most WSD systems use supervised methods to identify senses and tend to achieve the best results. However, supervised systems rely on manually annotated training corpora. Availability of manually tagged corpora is limited and generating these corpora is costly and time consuming. With our TreeMatch system, we use a fully unsupervised domain- independent method that only requires a dictionary (WordNet, Fallbaum, 1998.) and unannotated text as input (Chen et.al, 2009). WSD systems trained on general corpora tend to perform worse when disambiguating words from a document on a specific domain. The SemEval 2010 WSD-domain task (Agirre et. al., 2010) addresses this issue by testing participant systems on documents from the environment domain. The environment domain specific corpus for this task was built from documents contributed by the European Centre for Nature Conservation (ECNC) and the World Wildlife Fund (WWF). We adapted our existing TreeMatch system from running on a general context knowledge base to one targeted at the environment domain. This paper is organized as follows. Section 2 will detail the construction of the knowledge base. In Section 3 the WSD algorithm will be explained. The construction procedure and WSD algorithm described in these two sections are similar to the procedure presented in our NAACL 2009 paper (Chen et.al, 2009). In Section 4 we present our experiments and results, and Section 5 discusses related work on WSD. Section 6 finishes the paper with conclusions. 2 Context Knowledge Acquisition and Representation Figure 1 shows an overview of our context knowledge acquisition process. The collected knowledge is saved in a local knowledge base. Here are some details about each step. Figure 1: Context Knowledge Acquisition and Representation Process 2.1 Corpus Building Through Web Search The goal of this step is to collect as many valid sample sentences as possible that contain instances of the target word. Preferably these instances are also diverse enough to …

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