We describe our approach to the construction and evaluation of a large-scale database called "CatVar" which contains categorial variations of English lexemes. Due to the

Verbal and compositional lexical aspect provide the underlying temporal structure of events. Knowledge of lexical aspect, e.g.. (a)telicity, is therefore required for interpreting event sequences in dis- course (Dowry, 1986; Moens and Steed- man, 1988; Passoneau, 1988), interfacing to temporal databases (Androutsopoulos, 1996), processing temporal modifiers (Antonisse, 1994), describing allowable alternations and their semantic effects (Resnik, 1996; Tenny, 1994), and selecting tense and lexical items for natural language generation ((Dorr and Olsen, 1996; Klavans and Chodorow, 1992), cf. (Slobin and Bocaz, 1988)). We show that it is possible to represent lexical aspect--both verbal and compositional--on a large scale, using Lexical Conceptual Structure (LCS) representations of verbs in the classes cat- aloged by Levin (1993). Ve show how proper consideration of these universal pieces of verb meaning may be used to refine lexical representations and derive a range of meanings from combinations of LCS representations. A single algorithm may therefore be used to determine lexical aspect classes and features at both verbal and sentence levels. Finally, we illustrate how knowledge of lexical aspect facilitates the interpretation of events in NLP appli- cations.

In this article we investigate logical metonymy, that is, constructions in which the argument of a word in syntax appears to be different from that argument in logical form (e.g., enjoy the book means enjoy reading the book, and easy problem means a problem that is easy to solve). The systematic variation in the interpretation of such constructions suggests a rich and complex theory of composition on the syntax/semantics interface. Linguistic accounts of logical metonymy typically fail to describe exhaustively all the possible interpretations, or they don't rank those interpretations in terms of their likelihood. In view of this, we acquire the meanings of metonymic verbs and adjectives from a large corpus and propose a probabilistic model that provides a ranking on the set of possible interpretations. We identify the interpretations automatically by exploiting the consistent correspondences between surface syntactic cues and meaning. We evaluate our results against paraphrase judgments elicited experimentally from humans and show that the model's ranking of meanings correlates reliably with human intuitions.

This thesis aims at an automatic acquisition of a semantic classification for verbs. As starting point, I assume that the....

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This paper describes the automation of a new text categorization task. The categories assigned in this task are more syntactically, semantically, and contextually complex than those typically assigned by fully automatic systems that process unseen test data. Our system for assigning these categories uses a probabilistic classifier, developed with a recent method for formulating a probabilistic model from a predefined set of potential features (Bruce 1995, Bruce and Wiebe 1994, Pedersen et al. 1996). This paper focuses on feature selection. It presents various types of properties experimented with in this work. We identify and evaluate various approaches to organizing the collocational properties into features. With the more complex features we define, there is an organization that yields the best results; but the same organization with less complex features yields inferior results. The results suggest a way to take advantage of properties that are low frequency but ...

The study of the conceptual representations that underlie the use of language is a problem motivated from both a cognitive research point of view and that of construing language models for various language processing tasks. In this work, we organized 600 Finnish verbs using the SOM algorithm. Three experiments were conducted using dierent features to encode the verbs: morphosyntactic properties, individual nouns, and noun categories in the context of the verb. In general, the morphosyntactic properties seem to draw attention to semantic roles, whereas nouns as features seem to highlight clusters formed on grounds of topics in the text.

The importance of dealing with unknown words in Natural Language Processing (NLP) is growing as NLP systems are used in more and more applications. One aid in predicting the lexicai class of words that do not appear in the lexicon (referred to as unknown vords) is the use of syntactic parsing rules. The distinction between closed-class and open-class words together with morphological recognition appears to be pivotal in increasing the ability of the system to predict the lexical categories of unknown words. An experiment is performed to investigate the ability of a parser to parse unknown words using morphology and syntactic parsing rules without human intervention. This experiment shows that the performance of the parser is enhanced greatly when morphological recognition is used in conjunction with syntactic rules to parse sentences containing unknown words from the TIMIT corpus.

: Verbal and compositional lexical aspect provide the underlying temporal structure of events. Knowledge of lexical aspect, e.g., (a)telicity, is therefore required for interpreting event sequences in discourse (Dowty, 1986; Moens and Steedman, 1988; Passoneau, 1988), interfacing to temporal databases (Androutsopoulos, 1996), processing temporal modifiers (Antonisse, 1994), describing allowable alternations and their semantic effects (Resnik, 1996; Tenny, 1994), and selecting tense and lexical items for natural language generation ((Dorr and Olsen, 1996; Klavans and Chodorow, 1992), cf. (Slobin and Bocaz, 1988)). We show that it is possible to represent lexical aspect---both verbal and compositional---on a large scale, using Lexical Conceptual Structure (LCS) representations of verbs in the classes cataloged by Levin (1993). We show how proper consideration of these universal pieces of verb meaning may be used to refine lexical representations and derive a range of meanings from combin...

Morphological information is useful for parsing, lemmatization, and in