Supervised Domain Adaption for WSD

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 42–50, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Supervised Domain Adaption for WSD Eneko Agirre and Oier Lopez de Lacalle IXA NLP Group University of the Basque Country Donostia, Basque Contry {e.agirre,oier.lopezdelacalle}@ehu.es Abstract The lack of positive results on super- vised domain adaptation for WSD have cast some doubts on the utility of hand- tagging general corpora and thus devel- oping generic supervised WSD systems. In this paper we show for the first time that our WSD system trained on a general source corpus (BNC) and the target corpus, obtains up to 22% error reduction when compared to a system trained on the tar- get corpus alone. In addition, we show that as little as 40% of the target corpus (when supplemented with the source cor- pus) is sufficient to obtain the same results as training on the full target data. The key for success is the use of unlabeled data with SVD, a combination of kernels and SVM. 1 Introduction In many Natural Language Processing (NLP) tasks we find that a large collection of manually- annotated text is used to train and test supervised machine learning models. While these models have been shown to perform very well when tested on the text collection related to the training data (what we call the source domain), the perfor- mance drops considerably when testing on text from other domains (called target domains). In order to build models that perform well in new (target) domains we usually find two settings (Daum´e III, 2007). In the semi-supervised setting, the training hand-annotated text from the source domain is supplemented with unlabeled data from the target domain. In the supervised setting, we use training data from both the source and target domains to test on the target domain. In (Agirre and Lopez de Lacalle, 2008) we studied semi-supervised Word Sense Disambigua- tion (WSD) adaptation, and in this paper we fo- cus on supervised WSD adaptation. We compare the performance of similar supervised WSD sys- tems on three different scenarios. In the source to target scenario the WSD system is trained on the source domain and tested on the target do- main. In the target scenario the WSD system is trained and tested on the target domain (using cross-validation). In the adaptation scenario the WSD system is trained on both source and target domain and tested in the target domain (also using cross-validation over the target data). The source to target scenario represents a weak baseline for domain adaptation, as it does not use any exam- ples from the target domain. The target scenario represents the hard baseline, and in fact, if the do- main adaptation scenario does not yield better re- sults, the adaptation would have failed, as it would mean that the source examples are not useful when we do have hand-labeled target examples. Previous work shows that current state-of-the- art WSD systems are not able …

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