Coreference Resolution is a subfield of Information Extraction & Text Mining that deals with identifying all expressions that refer to the same entity in a text. In other words, it is the task of determining which pronouns, nouns, or phrases in a text refer to the same person, place, thing, or concept.

Evaluation refers to the process of determining the performance or accuracy of a model or algorithm. It involves comparing the model's predictions or outputs against a set of pre-defined correct results (ground truth or gold labels). The choice of evaluation method usually depends on the specific task or problem at hand.

Natural Language Processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Information Extraction & Text Mining is a subfield of Natural Language Processing (NLP) that focuses on automatically extracting structured information and knowledge from unstructured textual data.

Anaphora Resolution refers to the task of determining what a pronoun, or a noun phrase refers to in a given context. It's about resolving references in the text, especially when pronouns like 'he', 'she', 'it' are used, the system should be able to identify to whom or what these pronouns are referring to. This is crucial for understanding the semantics and the complete meaning of a text.

In this paper we argue that comparative evaluation in anaphora resolution has to be performed using the same pre-processing tools and on the same set of data. The paper proposes an evaluation environment for comparing anaphora resolution algorithms which is illustrated by presenting the results of the comparative evaluation of three methods on the basis of several evaluation measures.

Publication:

Evaluation Tool for Rule-based Anaphora Resolution Methods

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