Resolving Other-Anaphora

Reference resolution is a major component of any natural language system. In the past 30 years significant progress has been made in coreference resolution. However, there is more anaphora in texts than coreference. I present a computational treatment of other-anaphora, i.e., referential noun phrases (NPs) with non-pronominal heads modified by “other” or “another”: [. . . ] the move is designed to more accurately reflect the value of products and to put steel on more equal footing with other commodities. Such NPs are anaphoric (i.e., they cannot be interpreted in isolation), with an antecedent that may occur in the previous discourse or the speaker’s and hearer’s mutual knowledge. For instance, in the example above, the NP “other commodities” refers to a set of commodities excluding steel, and it can be paraphrased as “commodities other than steel”. Resolving such cases requires first identifying the correct antecedent(s) of the other-anaphors. This task is the major focus of this dissertation. Specifically, the dissertation achieves two goals. First, it describes a procedure by which antecedents of other-anaphors can be found, including constraints and preferences which narrow down the search. Second, it presents several symbolic, machine learning and hybrid resolution algorithms designed specifically for other-anaphora. All the algorithms have been implemented and tested on a corpus of examples from the Wall Street Journal. The major results of this research are the following: 1. Grammatical salience plays a lesser role in resolving other-anaphors than in resolving pronominal anaphora. Algorithms that solely rely on grammatical features achieved worse results than algorithms that used semantic features as well. 2. Semantic knowledge (such as “steel is a commodity”) is crucial in resolving other-anaphors. Algorithms that operate solely on semantic features outperformed those that operate on grammatical knowledge. 3. The quality and relevance of the semantic knowledge base is important to success. WordNet proved insufficient as a source of semantic information for resolving other-anaphora. Algorithms that use the Web as a knowledge base achieved