Type theoretic semantics for semantic networks: an application to natural language engineering

Semantic Networks have long been recognised as an important tool for natural language processing. This research has been a formal analysis of a semantic network using constructive type theory. The particular net studied is SemNet, the internal knowledge representation for LOLITA(^1): a large scale natural language engineering system. SemNet has been designed with large scale, efficiency, integration and expressiveness in mind. It supports many different forms of plausible and valid reasoning, including: epistemic reasoning, causal reasoning and inheritance. The unified theory of types (UTT) integrates two well known type theories, Coquand-Huet's (impredicative) calculus of constructions and Martin-Lof's (predicative) type theory. The result is a strong and expressive language which has been used for formalization of mathematics, program specification and natural language. Motivated by the computational and richly expressive nature of UTT, this research has used it for formalization and semantic analysis of SemNet. Moreover, because of applications to software engineering, type checkers/proof assistants have been built. These tools are ideal for organising and managing the analysis of SemNet. The contribution of the work is twofold. First the semantic model built has led to improved and deeper understanding of SemNet. This is important as many researchers that work on different aspects of LOLITA, now have a clear and un- ambigious interpertation of the meaning of SemNet constructs. The model has also been used to show soundess of the valid reasoning and to give a reasonable semantic account of epistemic reasoning. Secondly the research contributes to NLE generally, both because it demonstrates that UTT is a useful formalization tool and that the good aspects of SemNet have been formally presented

Interpretation of anaphoric expressions in the Lolita system

This thesis addresses the issue of anaphora resolution in the large scale natural language system, LOLITA. The work described here involved a thorough analysis of the system’s initial performance, the collection of evidence for and the design of the new anaphora resolution algorithm, and subsequent implementation and evaluation of the system. Anaphoric expressions are elements of a discourse whose resolution depends on other elements of the preceding discourse. The processes involved in anaphora resolution have long been the subject of research in a variety of fields. The changes carried out to LOLITA first involved substantial improvements to the core, lower level modules which form the basis of the system. A major change specific to the interpretation of anaphoric expressions was then introduced. A system of filters, in which potential candidates for resolution are filtered according to a set of heuristics, has been changed to a system of penalties, where candidates accumulate points throughout the application of the heuristics. At the end of the process, the candidate with the smallest penalty is chosen as a referent. New heuristics, motivated by evidence drawn from research in linguistics, psycholinguistics and AI, have been added to the system. The system was evaluated using a procedure similar to that defined by MUC6 (DARPA 1995). Blind and open tests were used. The first evaluation was carried out after the general improvements to the lower level modules; the second after the introduction of the new anaphora algorithm. It was found that the general improvements led to a considerable rise in scores in both the blind and the open test sets. As a result of the anaphora specific improvements, on the other hand, the rise in scores on the open set was larger than the rise on the blind set. In the open set the category of pronouns showed the most marked improvement. It was concluded that it is the work carried out to the basic, lower level modules of a large scale system which leads to biggest gains. It was also concluded that considerable extra advantage can be gained by using the new weights-based algorithm together with the generally improved system

An investigation into the application of machine learning in information retrieval

There is an increasing variety of online databases available which are also evergrowing in size. In retrieving information from these sources, it is important not only to have effective and efficient retrieval techniques but also to enable some form of adaptation to users’ specific needs. Frequent users, in particular, should be able to benefit from their high use of the information retrieval system. A machine learning approach can be applied to help the system adapt to users’ specific needs. It is argued that users have a particular context within which their queries are formed. It is likely that consecutive queries for a particular user will be related in that they will be part of the same context. Thus, a context learner is proposed. In this investigation, the context learner is used for enhancing document ordering in partial match systems