A New, Fully Automatic Version of Mitkov's Knowledge-Poor Pronoun Resolution Method

This paper describes a new, advanced and completely revamped version of Mitkov's knowledge-poor approach to pronoun resolution. In contrast to most anaphora resolution approaches, the new system, referred to as MARS, operates in fully automatic mode. It benefits from purpose-built programs for identifying occurrences of non-nominal anaphora (including pleonastic pronouns) and for recognition of animacy, and employs genetic algorithms to achieve optimal performance. The paper features extensive evaluation and discusses important evaluation issues in anaphora resolution

Putting Successor Variety Stemming to Work

Abstract. Stemming algorithms find canonical forms for inflected words, e. g. for declined nouns or conjugated verbs. Since such a unification of words with respect to gender, number, time, and case is a language-specific issue, stemming algorithms operationalize a set of linguistically motivated rules for the language in question. The most well-known rule-based algorithm for the English language is from Porter [14]. The paper presents a statistical stemming approach which is based on the analysis of the distribution of word prefixes in a document collection, and which thus is widely language-independent. In particular, our approach addresses the problem of index construction for multi-lingual documents. Related work for statistical stemming focuses either on stemming quality [2,3] or on runtime performance [11], but neither provides a reasonable tradeoff between both. For selected retrieval tasks under vector-based document models we report on new results related to stemming quality and collection size dependency. Interestingly, successor variety stemming has neither been investigated under similarity concerns for index construction nor is it applied as a technology in current retrieval applications. As our results will show, this disregard is not justified.

Automatic language-specific stemming in information retrieval

Abstract. We employ Automorphology, an MDL-based algorithm that determines the suffixes present in a language-sample with no prior knowledge of the language in question, and describe our experiments on the usefulness of this approach for Information Retrieval, employing this stemmer in a SMARTbased IR engine.