GAMBL is a word expert approach to WSD in which each word expert is trained using memorybased learning. Joint feature selection and algorithm parameter optimization are achieved with a genetic algorithm (GA). We use a cascaded classifier approach in which the GA optimizes local context features and the output of a separate keyword classifier (rather than also optimizing the keyword features together with the local context features). A further innovation on earlier versions of memorybased WSD is the use of grammatical relation and chunk features. This paper presents the architecture of the system briefly, and discusses its performance on the English lexical sample and all words tasks in SENSEVAL-3. We interpret WSD as a classification task distributed over word experts: given an ambiguous word and its context as input features, a classifier specialized on that word assigns the contextually appropriate sense to it. For each word-lemma-POS-tag combination, a separate classifier is trained. Information about the words immediately surrounding the ambiguous word (the local context), as well as information about sense-related words in a wider context (keywords) are provided as information sources, coded in a feature vector. To train the word experts, memory-based learning (MBL) is used, an instance of the lazy learning paradigm: all contexts in which an ambiguous word occurs in the training text are kept in memory and abstraction only occurs at classification time by extrapolating a class from the most similar item(s) in memory to the new test item. This contrasts with eager learning methods such as decision lists which abstract from the training data at training time and forget about the examples themselves. For our experiments, we use the MBL algorithms implemented in TIMBL 1 . This software 1 We used TIMBL version 5.0.0, which is available from http://ilk.kub.nl et al., 1999), that lazy learning has the right bias for learning natural language processing tasks as it makes possible learning from atypical and low-frequency events that are usually discarded by eager learning methods. Architecture. Previous work on memory-based WSD includes work from Ng and Lee (1996) , Veenstra et al. (2000), Hoste et al. (2002) and Mihalcea (2002) . The current design of our WSD system is largely based on Hoste et al. (2002) . Figure 1 gives an overview of the design of our WSD system: the training text is first linguistically analyzed. For each word-lemma-POS-tag combination, we check if it (i) is in our sense lexicon, (ii) has more than one sense and (iii) has a frequency in the training text above a certain threshold. For all combinations matching these three conditions, we train a word expert module. To all combinations with only one sense, or with more senses and a frequency below the threshold, we assign the default sense, which is respectively the only or most frequent sense in WordNet. The word expert module consists of two cascaded memory-based classifiers: the sense predicted by
The system developed by REF uses two cascaded memory-based classifiers, combined with the use of a genetic algorithm for joint parameter optimization and feature selection.
GAMBL, Genetic Algorithm Optimization Of Memory-Based WSD
SENSEVAL International Workshop On The Evaluation Of Systems For The Semantic Analysis Of Text
Computer Science