Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 99-110). Also available on the Internet.

Temporal relation classification is one of the most challenging areas of natural language processing. Advances in this area have direct relevance to improving practical applications, such as question-answering and summarization systems, as well as informing theoretical understanding of temporal meaning realization in language. With the development of annotated textual materials, this domain is now accessible to empirical machine-learning oriented approaches, where systems treat temporal relation processing as a classification problem: i.e. a decision as per which label (before, after, identity, etc) to assign to a pair (i, j) of event indices in a text. Most reported systems in this new research domain utilize classifiers that make decisions effectively in isolation, without explicitly utilizing the decisions made about other indices in a document. In this work, we present a new strategy for temporal relation classification that utilizes global models of temporal relations in a document, choosing the optimal classification for all pairs of indices in a document subject to global constraints which may be linguistically motivated. We propose and evaluate two applications of global models to temporal semantic processing: joint prediction of situation entities with temporal relations, and temporal relations prediction guided by global coherence constraints. / text

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No current laboratory test can reliably identify patients with schizophrenia. Instead, key symptoms are observed via language, including derailment, where patients cannot follow a coherent storyline, and delusions, where false beliefs are repeated as fact. Brain processes underlying these and other symptoms remain unclear, and characterizing them would greatly enhance our understanding of schizophrenia. In this situation, computational models can be valuable tools to formulate testable hypotheses and to complement clinical research. This dissertation aims to capture the link between biology and schizophrenic symptoms using DISCERN, a connectionist model of human story processing. Competing illness mechanisms proposed to underlie schizophrenia are simulated in DISCERN, and are evaluated at the level of narrative language, the same level used to diagnose patients. The result is the first simulation of a speaker with schizophrenia. Of all illness models, hyperlearning, a model of overly intense memory consolidation, produced the best fit to patient data, as well as compelling models of delusions and derailments. If validated experimentally, the hyperlearning hypothesis could advance the current understanding of schizophrenia, and provide a platform for simulating the effects of future treatments. / text

The subject matter of this thesis is the problem of learning to discover grammatical structure from raw text alone, without access to explicit instruction or annotation -- in particular, by a computer or computational process -- in other words, unsupervised parser induction, or simply, unsupervised parsing. This work presents a method for raw text unsupervised parsing that is simple, but nevertheless achieves state-of-the-art results on treebank-based direct evaluation. The approach to unsupervised parsing presented in this dissertation adopts a different way to constrain learned models than has been deployed in previous work. Specifically, I focus on a sub-task of full unsupervised partial parsing called unsupervised partial parsing. In essence, the strategy is to learn to segment a string of tokens into a set of non-overlapping constituents or chunks which may be one or more tokens in length. This strategy has a number of advantages: it is fast and scalable, based on well-understood and extensible natural language processing techniques, and it produces predictions about human language structure which are useful for human language technologies. The models developed for unsupervised partial parsing recover base noun phrases and local constituent structure with high accuracy compared to strong baselines. Finally, these models may be applied in a cascaded fashion for the prediction of full constituent trees: first segmenting a string of tokens into local phrases, then re-segmenting to predict higher-level constituent structure. This simple strategy leads to an unsupervised parsing model which produces state-of-the-art results for constituent parsing of English, German and Chinese. This thesis presents, evaluates and explores these models and strategies. / text

Parts of the thesis were published in: 1. Khoo, C., Myaeng, S.H., & Oddy, R. (2001). Using cause-effect relations in text to improve information retrieval precision. Information Processing and Management, 37(1), 119-145. 2. Khoo, C., Kornfilt, J., Oddy, R., & Myaeng, S.H. (1998). Automatic extraction of cause-effect information from newspaper text without knowledge-based inferencing. Literary & Linguistic Computing, 13(4), 177-186. 3. Khoo, C. (1997). The use of relation matching in information retrieval. LIBRES: Library and Information Science Research Electronic Journal [Online], 7(2). Available at: http://aztec.lib.utk.edu/libres/libre7n2/. An update of the literature review on causal relations in text was published in: Khoo, C., Chan, S., & Niu, Y. (2002). The many facets of the cause-effect relation. In R.Green, C.A. Bean & S.H. Myaeng (Eds.), The semantics of relationships: An interdisciplinary perspective (pp. 51-70). Dordrecht: Kluwer / This study represents one attempt to make use of relations expressed in text to improve information retrieval effectiveness. In particular, the study investigated whether the information obtained by matching causal relations expressed in documents with the causal relations expressed in users' queries could be used to improve document retrieval results in comparison to using just term matching without considering relations. An automatic method for identifying and extracting cause-effect information in Wall Street Journal text was developed. The method uses linguistic clues to identify causal relations without recourse to knowledge-based inferencing. The method was successful in identifying and extracting about 68% of the causal relations that were clearly expressed within a sentence or between adjacent sentences in Wall Street Journal text. Of the instances that the computer program identified as causal relations, 72% can be considered to be correct. The automatic method was used in an experimental information retrieval system to identify causal relations in a database of full-text Wall Street Journal documents. Causal relation matching was found to yield a small but significant improvement in retrieval results when the weights used for combining the scores from different types of matching were customized for each query -- as in an SDI or routing queries situation. The best results were obtained when causal relation matching was combined with word proximity matching (matching pairs of causally related words in the query with pairs of words that co-occur within document sentences). An analysis using manually identified causal relations indicate that bigger retrieval improvements can be expected with more accurate identification of causal relations. The best kind of causal relation matching was found to be one in which one member of the causal relation (either the cause or the effect) was represented as a wildcard that could match with any term. The study also investigated whether using Roget's International Thesaurus (3rd ed.) to expand query terms with synonymous and related terms would improve retrieval effectiveness. Using Roget category codes in addition to keywords did give better retrieval results. However, the Roget codes were better at identifying the non-relevant documents than the relevant ones. Parts of the thesis were published in: 1. Khoo, C., Myaeng, S.H., & Oddy, R. (2001). Using cause-effect relations in text to improve information retrieval precision. Information Processing and Management, 37(1), 119-145. 2. Khoo, C., Kornfilt, J., Oddy, R., & Myaeng, S.H. (1998). Automatic extraction of cause-effect information from newspaper text without knowledge-based inferencing. Literary & Linguistic Computing, 13(4), 177-186. 3. Khoo, C. (1997). The use of relation matching in information retrieval. LIBRES: Library and Information Science Research Electronic Journal [Online], 7(2). Available at: http://aztec.lib.utk.edu/libres/libre7n2/. An update of the literature review on causal relations in text was published in: Khoo, C., Chan, S., & Niu, Y. (2002). The many facets of the cause-effect relation. In R.Green, C.A. Bean & S.H. Myaeng (Eds.), The semantics of relationships: An interdisciplinary perspective (pp. 51-70). Dordrecht: Kluwer

This study is in the area of sentiment classification: classifying online review documents according to the overall sentiment expressed in them. This paper presents a prototype sentiment-based meta search engine that has been developed to perform sentiment categorization of Web search results. It assists users to quickly focus on recommended or non-recommended information by classifying Web search results into four categories: positive, negative, neutral, and non-review documents. It does this by using an automatic classifier based on a supervised machine learning algorithm, Support Vector Machine (SVM). This paper also discusses various issues we have encountered during the prototype development, and presents our approaches for resolving them. A user evaluation of the prototype was carried out with positive responses from users.

Humans are not often very skilled in asking good questions because of their inconsistent mind in certain situations. Thus, Question Generation (QG) and Question Answering (QA) became the two major challenges for the Natural Language Processing (NLP), Natural Language Generation (NLG), Intelligent Tutoring System, and Information Retrieval (IR) communities, recently. In this thesis, we consider a form of Sentence-to-Question generation task where given a sentence as input, the QG system would generate a set of questions for which the sentence contains, implies, or needs answers. Since the given sentence may be a complex sentence, our system generates elementary sentences from the input complex sentences using a syntactic parser. A Part of Speech (POS) tagger and a Named Entity Recognizer (NER) are used to encode necessary information. Based on the subject, verb, object and preposition information, sentences are classified in order to determine the type of questions to be generated. We conduct extensive experiments on the TREC-2007 (Question Answering Track) dataset. The scenario for the main task in the TREC-2007 QA track was that an adult, native speaker of English is looking for information about a target of interest. Using the given target, we filter out the important sentences from the large sentence pool and generate possible questions from them. Once we generate all the questions from the sentences, we perform a recall-based evaluation. That is, we count the overlap of our system generated questions with the given questions in the TREC dataset. For a topic, we get a recall 1.0 if all the given TREC questions are generated by our QG system and 0.0 if opposite. To validate the performance of our QG system, we took part in the First Question Generation Shared Task Evaluation Challenge, QGSTEC in 2010. Experimental analysis and evaluation results along with a comparison of different participants of QGSTEC'2010 show potential significance of our QG system. / x, 125 leaves : ill. ; 29 cm

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