In this work, we develop and evaluate a wide range of feature spaces for deriving Levinstyle verb classifications (Levin, 1993). We perform the classification experiments using

VerbNet (VN) is a major large-scale English verb lexicon. Mapping verb instances to their VN classes has been proven useful for several NLP tasks. However, verbs are polysemous with respect to their VN classes. We introduce a novel supervised learning model for mapping verb instances to VN classes, using rich syntactic features and class membership constraints. We evaluate the algorithm in both in-domain and corpus adaptation scenarios. In both cases, we use the manually tagged Semlink WSJ corpus as training data. For indomain (testing on Semlink WSJ data), we achieve 95.9 % accuracy, 35.1 % error reduction (ER) over a strong baseline. For adaptation, we test on the GENIA corpus and achieve 72.4 % accuracy with 10.7% ER. This is the first large-scale experimentation with automatic algorithms for this task. 1

The work presented in this paper explores a supervised method for learning a probabilistic model of a lexicon of VerbNet classes. We intend for the probabilistic model to provide a probability distribution of verb-class associations, over known and unknown verbs, including polysemous words. In our approach, training instances are obtained from an existing lexicon and/or from an annotated corpus, while the features, which represent syntactic frames, semantic similarity, and selectional preferences, are extracted from unannotated corpora. Our model is evaluated in type-level verb classification tasks: we measure the prediction accuracy of VerbNet classes for unknown verbs, and also measure the dissimilarity between the learned and observed probability distributions. We empirically compare several settings for model learning, while we vary the use of features, source corpora for feature extraction, and disambiguated corpora. In the task of verb classification into all VerbNet classes, our best model achieved a 10.69 % error reduction in the classification accuracy, over the previously proposed model. 1

Improved automatic text understanding requires detailed linguistic information about the words that comprise the text. Particularly crucial is the knowledge about predicates, typically verbs, which communicate both the event being expressed and how participants are related to the event. Although the field of natural language processing (NLP) has yet to develop a clear consensus on guidelines for building a verb lexicon suitable for applications in NLP, class-based construction of verb lexicons (e.g. Levin verb classification) with explicitly stated syntactic and semantic information has proved beneficial to a wide range of NLP tasks in combating the pervasive problem of data sparsity and increasing coverage. Such broad coverage dictionaries and ontologies are difficult and costly to create and maintain by hand, it is therefore desirable to learn them from distributional information, such as can be obtained from unlabeled or sparsely labeled text corpora. To this end, this thesis will primarily address the following three questions: First, deriving Levin-style verb classifications from text corpora helps avoid the expensive hand-coding of such information, but appropriate features must be identified and