Thesis (M.S. in Software Engineering)--Naval Postgraduate School, March 2003. / Thesis advisor(s): James Bret Michael, Neil C. Rowe. Includes bibliographical references (p. 91-92). Also available online.

Why do some groups of people work well together while others do not? It is commonly accepted that effective groups communicate well. Yet one of the biggest roadblocks facing the study of group communication is that it is extremely difficult to capture real-world group interactions and analyze the words people use in a timely manner. This project overcame this limitation in two ways. First, a broader and more systematic study of group processes was conducted by using a computerized text analysis program (Linguistic Inquiry and Word Count) that automatically codes natural language using pre-established rules. Groups that work well together typically exchange more knowledge and establish good social relationships, which is reflected in the way that they use words. The group dynamics of over 500 student discussion groups interacting via group chat were assessed by studying their language use. Second, a language feedback system was built to experimentally test the importance of certain group processes on group satisfaction and performance. It is now possible to provide language feedback by processing natural language dialogue using computerized text analysis in real time. The language feedback system can change the way the group works by providing individualized recommendations. In this way it is possible to manipulate group processes naturalistically. Together these studies provided evidence that important group processes can be detected even using simplistic natural language processing, and preliminary evidence that providing real-time feedback based on the words students use in a group discussion can improve learning by changing how the group works together. / text

published_or_final_version / Computer Science / Master / Master of Philosophy

Name Search is an important search function in various types of information retrieval systems, such as online library catalogs and electronic yellow pages. It is also difficult due to the high degree of fuzziness required in matching name variants. Previous approaches to name search systems use ad hoc combinations of search heuristics. This paper first discusses two approaches to name modelingâ the natural language processing (NLP) and the information retrieval (IR) modelsâ and proposes a hybrid approach. The approach demonstrates a critical combination of complementary NLP and IR features that produces more effective fuzzy name matching. Two principles, position-as-attribute and position-transitionlikelihood, are introduced as the principles for integrating the advantageous aspects of both approaches. They have been implemented in an NLP- and IR- hybrid model system called Friendly Name Search (FNS) for real world applications in multilingual directory searches on the Singapore Yellow pages website.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Natural language processing for biomedical text currently focuses mostly on entity and relation extraction. These entities and relations are usually pre-specified entities, e.g., proteins, and pre-specified relations, e.g., inhibit relations. A shallow parser that captures the relations between noun phrases automatically from free text has been developed and evaluated. It uses heuristics and a noun phraser to capture entities of interest in the text. Cascaded finite state automata structure the relations between individual entities. The automata are based on closed-class English words and model generic relations not limited to specific words. The parser also recognizes coordinating conjunctions and captures negation in text, a feature usually ignored by others. Three cancer researchers evaluated 330 relations extracted from 26 abstracts of interest to them. There were 296 relations correctly extracted from the abstracts resulting in 90% precision of the relations and an average of 11 correct relations per abstract.

An approach to biomedical language processing is to apply existing natural language processing (NLP) solutions to biomedical texts. Often, existing NLP solutions are less successful in the biomedical domain relative to their non-biomedical domain performance (e.g., relative to newspaper text). Biomedical NLP is likely best served by methods, information and tools that account for its particular challenges. In this thesis, I describe an NLP system specifically engineered for sentinel event extraction from clinical documents. The NLP system's design accounts for several biomedical NLP challenges. The specific contributions are as follows. - Biomedical tokenizers differ, lack consensus over output tokens and are difficult to extend. I developed an extensible tokenizer, providing a tokenizer design pattern and implementation guidelines. It evaluated as equivalent to a leading biomedical tokenizer (MedPost). - Biomedical part-of-speech (POS) taggers are often trained on non-biomedical corpora and applied to biomedical corpora. This results in a decrease in tagging accuracy. I built a token centric POS tagger, TcT, that is more accurate than three existing POS taggers (mxpost, TnT and Brill) when trained on a non-biomedical corpus and evaluated on biomedical corpora. TcT achieves this increase in tagging accuracy by ignoring previously assigned POS tags and restricting the tagger's scope to the current token, previous token and following token. - Two parsers, MST and Malt, have been evaluated using perfect POS tag input. Given that perfect input is unlikely in biomedical NLP tasks, I evaluated these two parsers on imperfect POS tag input and compared their results. MST was most affected by imperfectly POS tagged biomedical text. I attributed MST's drop in performance to verbs and adjectives where MST had more potential for performance loss than Malt. I attributed Malt's resilience to POS tagging errors to its use of a rich feature set and a local scope in decision making. - Previous automated clinical coding (ACC) research focuses on mapping narrative phrases to terminological descriptions (e.g., concept descriptions). These methods make little or no use of the additional semantic information available through topology. I developed a token-based ACC approach that encodes tokens and manipulates token-level encodings by mapping linguistic structures to topological operations in SNOMED CT. My ACC method recalled most concepts given their descriptions and performed significantly better than MetaMap. I extended my contributions for the purpose of sentinel event extraction from clinical letters. The extensions account for negation in text, use medication brand names during ACC and model (coarse) temporal information. My software system's performance is similar to state-of-the-art results. Given all of the above, my thesis is a blueprint for building a biomedical NLP system. Furthermore, my contributions likely apply to NLP systems in general. / Graduate

No description available.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 143-146). Also available via the Internet.

Thesis (M.S. in Computer Science)--Naval Postgraduate School, June 1990. / Thesis Advisor(s): Lum, Vincent Y. ; Rowe, Neil C. "June 1990." Description based on signature page. DTIC Identifiers: Interfaces, natural language, databases, theses. Author(s) subject terms: Natural language processing, multimedia database system, natural language interface, descriptive captions. Includes bibliographical references (p. 27).

Thesis (Ph. D.)--University of Cambridge, 2000. / Cover title. "September 2000." Includes bibliographical references.