Algorithms for Minimum Risk Chunking

Stochastic finite automata are useful for identifying substrings (chunks) within larger units of text. Relevant applications include tokenization, base-NP chunking, named entity recognition, and other information extraction tasks. For a given input string, a stochastic automaton represents a probability distribution over strings of labels encoding the location of chunks. For chunking and extraction tasks, the quality of predictions is evaluated in terms of precision and recall of the chunked/extracted phrases when compared against some gold standard. However, traditional methods for estimating the parameters of a stochastic finite automaton and for decoding the best hypothesis do not pay attention to the evaluation criterion, which we take to be the well-known F-measure. We are interested in methods that remedy this situation, both in training and decoding. Our main result is a novel algorithm for efficiently evaluating expected F-measure. We present the algorithm and discuss its applications for utility/ risk-based parameter estimation and decoding

Using Timed-Release Cryptography to Mitigate Preservation Risk of Embargo Periods

This research defines Time-Locked Embargo, a framework designed to mitigate the Preservation Risk Interval: the preservation risk associated with embargoed scholarly material. Due to temporary access restrictions, embargoed data cannot be distributed freely and thus preserved via data refreshing during the embargo time interval. A solution to mitigate the risk of data loss has been developed by suggesting a data dissemination framework that allows data refreshing of encrypted instances of embargoed content in an open, unrestricted scholarly community. This framework has been developed by exploiting implementations of existing technologies to time-lock data using Timed-Release Cryptology (TRC) so that it can be deployed s digital resources encoded in the MPEG-21 Digital Item Description Language (DIDL) complex object format to harvesters interested in harvesting a local copy of content by utilizing The Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), a widely accepted interoperability standard for the exchange of metadata. The framework successfully demonstrates dynamic record identification, time-lock puzzle (TLP) encryption, encapsulation and dissemination as XML documents. This thesis dissertation presents the framework architecture and provides a quantitative analysis of an implementation. The framework demonstrates successful data harvest of time-locked embargoed data with minimum time overhead without compromising data security and integrity

Ontology Enrichment from Free-text Clinical Documents: A Comparison of Alternative Approaches

While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships, as well as difficulty in updating the ontology as domain knowledge changes. Methodologies developed in the fields of Natural Language Processing (NLP), Information Extraction (IE), Information Retrieval (IR), and Machine Learning (ML) provide techniques for automating the enrichment of ontology from free-text documents. In this dissertation, I extended these methodologies into biomedical ontology development. First, I reviewed existing methodologies and systems developed in the fields of NLP, IR, and IE, and discussed how existing methods can benefit the development of biomedical ontologies. This previously unconducted review was published in the Journal of Biomedical Informatics. Second, I compared the effectiveness of three methods from two different approaches, the symbolic (the Hearst method) and the statistical (the Church and Lin methods), using clinical free-text documents. Third, I developed a methodological framework for Ontology Learning (OL) evaluation and comparison. This framework permits evaluation of the two types of OL approaches that include three OL methods. The significance of this work is as follows: 1) The results from the comparative study showed the potential of these methods for biomedical ontology enrichment. For the two targeted domains (NCIT and RadLex), the Hearst method revealed an average of 21% and 11% new concept acceptance rates, respectively. The Lin method produced a 74% acceptance rate for NCIT; the Church method, 53%. As a result of this study (published in the Journal of Methods of Information in Medicine), many suggested candidates have been incorporated into the NCIT; 2) The evaluation framework is flexible and general enough that it can analyze the performance of ontology enrichment methods for many domains, thus expediting the process of automation and minimizing the likelihood that key concepts and relationships would be missed as domain knowledge evolves

Complexity of Lexical Descriptions and its Relevance to Partial Parsing

In this dissertation, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions (supertags) that impose complex constraints in a local context. However, increasing the complexity of descriptions makes the number of different descriptions for each lexical item much larger and hence increases the local ambiguity for a parser. This local ambiguity can be resolved by using supertag co-occurrence statistics collected from parsed corpora. We have explored these ideas in the context of Lexicalized Tree-Adjoining Grammar (LTAG) framework wherein supertag disambiguation provides a representation that is an almost parse. We have used the disambiguated supertag sequence in conjunction with a lightweight dependency analyzer to compute noun groups, verb groups, dependency linkages and even partial parses. We have shown that a trigram-based supertagger achieves an accuracy of 92.1‰ on Wall Street Journal (WSJ) texts. Furthermore, we have shown that the lightweight dependency analysis on the output of the supertagger identifies 83‰ of the dependency links accurately. We have exploited the representation of supertags with Explanation-Based Learning to improve parsing effciency. In this approach, parsing in limited domains can be modeled as a Finite-State Transduction. We have implemented such a system for the ATIS domain which improves parsing eciency by a factor of 15. We have used the supertagger in a variety of applications to provide lexical descriptions at an appropriate granularity. In an information retrieval application, we show that the supertag based system performs at higher levels of precision compared to a system based on part-of-speech tags. In an information extraction task, supertags are used in specifying extraction patterns. For language modeling applications, we view supertags as syntactically motivated class labels in a class-based language model. The distinction between recursive and non-recursive supertags is exploited in a sentence simplification application