Thesis--University of Michigan. / Includes bibliographical references (p. 150-151).

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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

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Summarization is a complex task that requires understanding of the document content to determine the importance of the text. Lexical cohesion is a method to identify connected portions of the text based on the relations between the words in the text. Lexical cohesive relations can be represented using lexical chaings. Lexical chains are sequences of semantically related words spread over the entire text. Lexical chains are used in variety of Natural Language Processing (NLP) and Information Retrieval (IR) applications. In current thesis, we propose a lexical chaining method that includes the glossary relations in the chaining process. These relations enable us to identify topically related concepts, for instance dormitory and student, and thereby enhances the identification of cohesive ties in the text. We then present methods that use the lexical chains to generate summaries by extracting sentences from the document(s). Headlines are generated by filtering the portions of the sentences extracted, which do not contribute towards the meaning of the sentence. Headlines generated can be used in real world application to skim through the document collections in a digital library. Multi-document summarization is gaining demand with the explosive growth of online news sources. It requires identification of the several themes present in the collection to attain good compression and avoid redundancy. In this thesis, we propose methods to group the portions of the texts of a document collection into meaningful clusters. clustering enable us to extract the various themes of the document collection. Sentences from clusters can then be extracted to generate a summary for the multi-document collection. Clusters can also be used to generate summaries with respect to a given query. We designed a system to compute lexical chains for the given text and use them to extract the salient portions of the document. Some specific tasks considered are: headline generation, multi-document summarization, and query-based summarization. Our experimental evaluation shows that efficient summaries can be extracted for the above tasks. / viii, 80 leaves : ill. ; 29 cm.

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The aim of this thesis is to improve coreference resolution in Swedish by providing a hybrid approach based on combining data-driven methods and linguistic knowledge. Coreference resolution here consists in identifying all expressions in a text that have the same referent, for example, a person or an object. The linguistic knowledge is based on Accessibility Theory (Ariel 1990). This is used for guiding the  selection of likely anaphor-antecedent pairs from the set of all possible such pairs in a text. The data-driven method adopted is Memory-Based Learning (MBL), a supervised method based on the idea that learning means storing experiences in memory, and that new problems are solved by reusing solutions from similar experiences (Daelemans and Van den Bosch 2005). The referring expressions covered by the system are names, definite descriptions, and pronouns. In order to maximize performance, we use different classifiers with a specific set of linguistically motivated features for each type of expression. The great majority of features used for classification are domain- and language-independent. We demonstrate two ways of using this method of linguistically motivated selection of anaphor-antecedent pairs. First, the amount of training examples stored in memory  is reduced. We find that for coreference resolution of definite descriptions and names, the amount of training data can thereby be reduced with only a minor loss in performance, but for pronoun resolution there is a negative effect. Second, selection can be used for improving on coreference resolution results. This is the first step in our hybrid approach to coreference resolution, where the second step is the application of an MBL classifier for determining coreference between the selected pairs. Results indicate that this hybrid approach is advantageous for coreference resolution of definite descriptions and names. For pronoun resolution, there is a negative effect on recall along with a positive effect on precision. / För att köpa boken skicka en beställning till exp@ling.su.se/ To order the book send an e-mail to exp@ling.su.se