Durham - a word sense disambiguation system

Ever since the 1950's when Machine Translation first began to be developed, word sense disambiguation (WSD) has been considered a problem to developers. In more recent times, all NLP tasks which are sensitive to lexical semantics potentially benefit from WSD although to what extent is largely unknown. The thesis presents a novel approach to the task of WSD on a large scale. In particular a novel knowledge source is presented named contextual information. This knowledge source adopts a sub-symbolic training mechanism to learn information from the context of a sentence which is able to aid disambiguation. The system also takes advantage of frequency information and these two knowledge sources are combined. The system is trained and tested on SEMCOR. A novel disambiguation algorithm is also developed. The algorithm must tackle the problem of a large possible number of sense combinations in a sentence. The algorithm presented aims to make an appropriate choice between accuracy and efficiency. This is performed by directing the search at a word level. The performance achieved on SEMCOR is reported and an analysis of the various components of the system is performed. The results achieved on this test data are pleasing, but are difficult to compare with most of the other work carried out in the field. For this reason the system took part in the SENSEVAL evaluation which provided an excellent opportunity to extensively compare WSD systems. SENSEVAL is a small scale WSD evaluation using the HECTOR lexicon. Despite this, few adaptations to the system were required. The performance of the system on the SENSEVAL task are reported and have also been presented in [Hawkins, 2000]

Doctor of Philosophy

dissertationDomain adaptation of natural language processing systems is challenging because it requires human expertise. While manual e ort is e ective in creating a high quality knowledge base, it is expensive and time consuming. Clinical text adds another layer of complexity to the task due to privacy and con dentiality restrictions that hinder the ability to share training corpora among di erent research groups. Semantic ambiguity is a major barrier for e ective and accurate concept recognition by natural language processing systems. In my research I propose an automated domain adaptation method that utilizes sublanguage semantic schema for all-word word sense disambiguation of clinical narrative. According to the sublanguage theory developed by Zellig Harris, domain-speci c language is characterized by a relatively small set of semantic classes that combine into a small number of sentence types. Previous research relied on manual analysis to create language models that could be used for more e ective natural language processing. Building on previous semantic type disambiguation research, I propose a method of resolving semantic ambiguity utilizing automatically acquired semantic type disambiguation rules applied on clinical text ambiguously mapped to a standard set of concepts. This research aims to provide an automatic method to acquire Sublanguage Semantic Schema (S3) and apply this model to disambiguate terms that map to more than one concept with di erent semantic types. The research is conducted using unmodi ed MetaMap version 2009, a concept recognition system provided by the National Library of Medicine, applied on a large set of clinical text. The project includes creating and comparing models, which are based on unambiguous concept mappings found in seventeen clinical note types. The e ectiveness of the nal application was validated through a manual review of a subset of processed clinical notes using recall, precision and F-score metrics

Online Crowds Opinion-Mining it to Analyze Current Trend: A Review

Online presence of the user has increased, there is a huge growth in the number of active users and thus the volume of data created on the online social networks is massive. Much are concentrating on the Internet Lingo. Notably most of the data on the social networking sites is made public which opens doors for companies, researchers and analyst to collect and analyze the data. We have huge volume of opinioned data available on the web we have to mine it so that we could get some interesting results out of it with could enhance the decision making process. In order to analyze the current scenario of what people are thinking focus is shifted towards opinion mining. This study presents a systematic literature review that contains a comprehensive overview of components of opinion mining, subjectivity of data, sources of opinion, the process and how does it let one analyze the current tendency of the online crowd in a particular context. Different perspectives from different authors regarding the above scenario have been presented. Research challenges and different applications that were developed with the motive opinion mining are also discussed

Word sense disambiguation : Scaling up, domain adaptation and application to machine translation

Ph.DDOCTOR OF PHILOSOPH