We present a machine learning framework for resolving other-anaphora. Besides morpho-syntactic, recency, and semantic features based on existing lexical 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 innovative feature leads to an 11.4 percentage point improvement in the classifier's-measure (25% improvement relative to results without this feature).
We report on an analysis of the use of THIS-NPs, i.e., noun phrases with the determiner this and the demonstrative pronouns this and these. We test the THIS-NP hypothesis, a refined and clarified summary of earlier proposals, such as (Linde, 1979; Gundel, Hedberg, and Zacharski, 1993; Passonneau, 1993), by way of a systematic analysis of the uses of these NPs in two different genres. In order to carry out the analysis, we devised a reliable annotation scheme for classifying THIS-NPs in our corpus as active or not. 92% of THIS-NPs in our corpus were classified as referring to entities which are active in this sense. We tested three formalizations of the THIS-NP hypothesis. The version that received most empirical support is the following: THIS-NPs are used to refer to entities which are active but not the backward-looking center of the previous utterance.
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