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Text mining methods strive to make unstructured text more useful for decision making. As part of the mining process, language is processed prior to analysis. Processing techniques have often focused primarily on either text structure or text meaning in preparing documents for analysis. As approaches have evolved over the years, increases in the use of lexical semantic parsing usually have come at the expense of full syntactic parsing. This work explores the benefits of combining structure and meaning or syntax and lexical semantics to support the text mining process.Chapter two presents the Arizona Summarizer, which includes several processing approaches to automatic text summarization. Each approach has varying usage of structural and lexical semantic information. The usefulness of the different summaries is evaluated in the finding stage of the text mining process. The summary produced using structural and lexical semantic information outperforms all others in the browse task. Chapter three presents the Arizona Relation Parser, a system for extracting relations from medical texts. The system is a grammar-based system that combines syntax and lexical semantic information in one grammar for relation extraction. The relation parser attempts to capitalize on the high precision performance of semantic systems and the good coverage of the syntax-based systems. The parser performs in line with the top reported systems in the literature. Chapter four presents the Arizona Entity Finder, a system for extracting named entities from text. The system greatly expands on the combination grammar approach from the relation parser. Each tag is given a semantic and syntactic component and placed in a tag hierarchy. Over 10,000 tags exist in the hierarchy. The system is tested on multiple domains and is required to extract seven additional types of entities in the second corpus. The entity finder achieves a 90 percent F-measure on the MUC-7 data and an 87 percent F-measure on the Yahoo data where additional entity types were extracted.Together, these three chapters demonstrate that combining text structure and meaning in algorithms to process language has the potential to improve the text mining process. A lexical semantic grammar is effective at recognizing domain-specific entities and language constructs. Syntax information, on the other hand, allows a grammar to generalize its rules when possible. Balancing performance and coverage in light of the world's growing body of unstructured text is important.

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