A modular framework for coreference resolution
Hamidreza Kobdani · OPUS Publication Server of the University of Stuttgart (University of Stuttgart) · 2012
Coreference resolution is playing an increasingly important role in a wide range of disciplines such as theoretical, corpus and computational linguistics. It has been shown that it is beneficial in a number of natural language processing applications, including machine translation, automatic abstracting, information extraction and question answering. As a result, it has enjoyed increased interest in recent years. First, this thesis introduces a modular supervised system for coreference resolution. It is composed of separate, interchangeable components, between which there are clear well-defined logical boundaries that improve maintainability. This system has been used successfully in two international shared tasks on coreference resolution achieving good performance. The good performance of our system demonstrates the general validity of our design. In addition, a new framework for feature engineering of natural language processing will be presented that is based on a relational data model of text. It includes fast and flexible methods for implementing and extracting new features, thereby reducing the effort of creating an NLP system for a particular task. This thesis presents an instantiation and evaluation of the framework for the problem of coreference resolution in multiple languages. Competitive results were able to be obtained in a short implementation period. This demonstrates the potential power of this framework for feature engineering. An unsupervised framework will also be presented that bootstraps a complete coreference resolution system from word associations mined from a large unlabeled corpus. I will show that word associations are useful for coreference resolution - e.g., the strong association between Obama and President is an indicator of likely coreference. Association information has so far not been used in coreference resolution because it is sparse and difficult to learn from small labeled corpora. Since unlabeled text is readily available, the unsupervised approach proposed here addresses the sparseness problem. In a self-training framework, I train a decision tree on a corpus that is automatically labeled using word associations. I will show that this unsupervised system has better coreference resolution performance than other learning approaches that do not use manually labeled data.