Using the Web in Machine Learning for Other-Anaphora Resolution

Using the Web in Machine Learning for Other-Anaphora Resolution Natalia N. Modjeska School of Informatics University of Edinburgh and Department of Computer Science University of Toronto natalia@cs.utoronto.ca Katja Markert School of Computing University of Leeds and School of Informatics University of Edinburgh markert@inf.ed.ac.uk Malvina Nissim School of Informatics University of Edinburgh mnissim@inf.ed.ac.uk Abstract We present a machine learning frame- work for resolving other-anaphora. Be- sides morpho-syntactic, recency, and se- mantic features based on existing lexi- cal knowledge resources, our algorithm obtains additional semantic knowledge from the Web. We search the Web via lexico-syntactic patterns that are specific to other-anaphors. Incorporating this in- novative feature leads to an 11.4 percent- age point improvement in the classifier’s F-measure (25% improvement relative to results without this feature). 1 Introduction Other-anaphors are referential NPs with the mod- ifiers “other” or “another” and non-structural an- tecedents:1 (1) An exhibition of American design and architec- ture opened in September in Moscow and will travel to eight other Soviet cities. (2) [. . . ] the alumni director of a Big Ten university “I’d love to see sports cut back and so would a lot of my counterparts at other schools, [. . . ]” (3) You either believe Seymour can do it again or you don’t. Beside the designer’s age, other risk factors for Mr. Cray’s company include the Cray-3’s [. . . ] chip technology. 1All examples are from the Wall Street Journal; the correct antecedents are in italics and the anaphors are in bold font. In (1), “eight other Soviet cities” refers to a set of So- viet cities excluding Moscow, and can be rephrased as “eight Soviet cities other than Moscow”. In (2), “other schools” refers to a set of schools excluding the mentioned Big Ten university. In (3), “other risk factors for Mr. Cray’s company” refers to a set of risk factors excluding the designer’s age. In contrast, in list-contexts such as (4), the an- tecedent is available both anaphorically and struc- turally, as the left conjunct of the anaphor.2 (4) Research shows AZT can relieve dementia and other symptoms in children [. . . ] We focus on cases such as (1–3). Section 2 describes a corpus of other-anaphors. We present a machine learning approach to other- anaphora, using a Naive Bayes (NB) classifier (Sec- tion 3) with two different feature sets. In Section 4 we present the first feature set (F1) that includes standard morpho-syntactic, recency, and string com- parison features. However, there is evidence that, e.g., syntactic features play a smaller role in resolv- ing anaphors with full lexical heads than in pronom- inal anaphora (Strube, 2002; Modjeska, 2002). In- stead, a large and diverse amount of lexical or world knowledge is necessary to understand exam- ples such as (1–3), e.g., that Moscow is a (Soviet) city, that universities are informally called schools in American English and that age can be viewed as a risk factor. Therefore we add lexical knowledge, which is extracted from WordNet (Fellbaum, 1998) and …

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