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this paper we argue that scientific articles require a different summarization strategy than, for instance, news articles. We propose a strategy which concentrates on the rhetorical status of statements in the article: Material for summaries is selected in such a way that summaries can highlight the new contribution of the source paper and situate it with respect to earlier work. We provide a gold standard for summaries of this kind consisting of a substantial corpus of conference articles in computational linguistics with human judgements of rhetorical status and relevance. We present several experiments measuring our judges' agreement on these annotations. We also present an algorithm which, on the basis of the annotated training material, selects content and classifies it into a fixed set of seven rhetorical categories. The output of this extraction and classification system can be viewed as a single-document summary in its own right; alternatively, it can be used to generate task-oriented and user-tailored summaries designed to give users an overview of a scientific field.

this paper, we focus on argument structure--the thematic roles assigned by a verb to its arguments--as the way in which the relational semantics of the verb is represented at the syntactic level

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Manual development of large subcategorised lexicons has proved difficult because predicates change behaviour between sublanguages, domains and over time. Yet access to a comprehensive subcategorization lexicon is vital for successful parsing capable of recovering predicate-argument relations, and probabilistic parsers would greatly benefit from accurate information concerning the relative likelihood of different subcategorisation frames (scfs) of a given predicate. Acquisition of subcategorization lexicons from textual corpora has recently become increasingly popular. Although this work has met with some success, resulting lexicons indicate a need for greater accuracy. One significant source of error lies in the statistical filtering used for hypothesis selection, i.e. for removing noise from automatically acquired scfs. This thesis builds on earlier work in verbal subcategorization acquisition, taking as a starting point the problem with statistical filtering. Our investigation shows that statistical filters tend to work poorly because not only is the underlying distribution zipfian, but there is also very little correlation between conditional distribution of

Word sense disambiguation assumes word senses. Within the lexicography and linguistics literature, they are known to be very slippery entities. The paper looks at problems with existing accounts of `word sense' and describes the various kinds of ways in which a word's meaning can deviate from its core meaning. An analysis is presented in which word senses are abstractions from clusters of corpus citations, in accordance with current lexicographic practice. The corpus citations, not the word senses, are the basic objects in the ontology. The corpus citations will be clustered into senses according to the purposes of whoever or whatever does the clustering. In the absence of such purposes, word senses do not exist. Word sense disambiguation also needs a set of word senses to disambiguate between. In most recent work, the set has been taken from a general-purpose lexical resource, with the assumption that the lexical resource describes the word senses of English/French/. . . , between whi...

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We present results of two methods for assessing the event profile of news articles as a function of verb type. The unique contribution of this research is the focus on the role of verbs, rather than nouns. Two algorithms are presented and evaluated, one of which is shown to accurately discriminate documents by type and semantic properties, i.e. the event profile. The initial method, using WordNet (Miller et al. 1990), produced nmltiple cross-classification of arti-cles, primarily due to the bushy nature of the verb tree coupled with the sense disambiguation problem. Ore " second approach using English Verb Classes and Alternations (EVCA) Levin (1993) showed that monosemous categorization of the frequent verbs in WSJ made it possible to usefully discriminate documents. For example, our results show that articles in which commu-nication verbs predominate tend to be opinion pieces, whereas articles with a high percentage of agreement verbs tend to be about mergers or legal cases. An evaluation is performed on the results using Kendall's 7. We present convinc-ing evidence for using verb semantic classes as a discriminant in document classification. 1 1

Verbs were clustered semantically on the basis of their alternation behariota', as characterised by l,heir synl,acLi(: subcaLegorisation lYame, s exLra(:Lcd from maximran proba,biliLy 1)arses ()[ a robttsL sl,adsLical 1)arsel ', ;red (:Oml)let,ed by assignin/5 WordNet, classes as select, ional preth'enees t,o t, he fi'ame argumenLs. The clustering was achieved (a.) iLeratively by mea.- sm'ing Lhe relal;ive ent,ropy bet,ween t.he verbs' ability (lisl.ribut, ions over the frame tyl)cS, and (b) l)y ul,ilising a latenl, class mm.lysis based on the joint fi'eqmm(:ies of verbs and frame types.

This thesis develops a new approach to automatic language generation that focuses on the need to produce a range of different paraphrases from the same input representation. One novelty of the system is its solidly grounding representations of word meaning in a background knowledge base, which enables the production of paraphrases stemming from certain inferences, rather than from purely lexical relationships alone. The system is designed in such a way that the paraphrasing mechanism extends naturally to a multilingual generator; specifically, we will be concerned with producing English and German sentences. The focus of the system is on lexical paraphrases, and one of the contributions of the thesis is in identifying, analyzing and extending relevant linguistic research so that it can be used to handle...