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| Autor/in | Li, Dingcheng |
|---|---|
| Titel | Entity Relation Detection with Factorial Hidden Markov Models and Maximum Entropy Discriminant Latent Dirichlet Allocations |
| Quelle | (2011), (138 Seiten)
PDF als Volltext Ph.D. Dissertation, University of Minnesota |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| ISBN | 978-1-2671-6867-2 |
| Schlagwörter | Hochschulschrift; Dissertation; Information Retrieval; Form Classes (Languages); Computation; Natural Language Processing; Markov Processes; Computational Linguistics; Online Searching; Database Design; Computer System Design; Artificial Intelligence; Programming; Models |
| Abstract | Coreference resolution (CR) and entity relation detection (ERD) aim at finding predefined relations between pairs of entities in text. CR focuses on resolving identity relations while ERD focuses on detecting non-identity relations. Both CR and ERD are important as they can potentially improve other natural language processing (NLP) related tasks such information retrieval and extraction, web-searching, and question answering and also enhance non-NLP tasks such as computer vision, database constructions or ontologies. In this thesis, I propose models to handle both coreference resolution (CR) and entity relation detection (ERD). Both systems are built on machine learning models. The CR system is based on Factorial Hidden Markov Models (FHMMs). The ERD is based on Maximum Entropy Discriminant Latent Dirichlet Allocation (MEDLDA). The work on CR only resolves pronouns. It is a supervised system trained on annotated corpus. The basic idea is that the hidden states of FHMMs are an explicit short-term memory with an antecedent buffer containing recently described referents. Thus an observed pronoun can find its antecedent from the hidden buffer, or in terms of a generative model, the entries in the hidden buffer generate the corresponding pronouns. In the hidden buffer, all references are expressed as diverse features. In this work, besides the common gender, number, person and animacy, I converted Givenness Hierarchy and Centering Theories to probabilistic features, thus greatly improving the accuracy. A system implementing this model is evaluated on the ACE corpus and I2B2 medical corpus with promising performance. For ERD, a novel application of topic models is proposed to do this task. In order to make use of the latent semantics of text, the task of relation detection is reformulated as a topic modeling problem. The motivation is to find underlying topics which are indicative of relations between named entities. The approach considers pairs of named entities and features associated with them as mini documents. The system, called ERD-MEDLDA, adapts Maximum Entropy Discriminant Latent Dirichlet Allocation (MedLDA) with mixed membership for relation detection. By using supervision, ERD-MedLDA is able to learn topic distributions indicative of relation types. Further, ERD-MEDLDA is a topic model that combines the benefits of both Maximum Likelihood Estimation (MLE) and Maximum Margin Estimation (MME), and the mixed membership formulation enables the system to incorporate heterogeneous features. We incorporate diverse features into the system and perform experiments on the ACE 2005 corpus. Our approach achieves better overall performance for precision, recall and Fmeasure metrics as compared to SVM-based and LDA-based models. ERD-MedLDA also shows better overall performance than state-of-the-art kernels used previously for relation detection. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided). |
| Anmerkungen | ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2017/4/10 |
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