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

Illuminating Trouble Tickets with Sublanguage Theory

A study was conducted to explore the potential of Natural Language Processing (NLP)based knowledge discovery approaches for the task of representing and exploiting the vital information contained in field service (trouble) tickets for a large utility provider. Analysis of a subset of tickets, guided by sublanguage theory, identified linguistic patterns, which were translated into rule-based algorithms for automatic identification of tickets ’ discourse structure. The subsequent data mining experiments showed promising results, suggesting that sublanguage is an effective framework for the task of discovering the historical and predictive value of trouble ticket data.

Trust in the context of subscription contracts

Trust plays an essential role in interorganizational interactions. It reduces uncertainty, ensures long-term relationships, positively influences innovation, product adoption, and serves as a solution to the commitment problem. This work observes trust in the context of a Software as a Service (SaaS) market. In a case study of a SaaS service provider and their customers, I apply the Ability, Benevolence, Integrity trust framework to illustrate the effect of individual trust dimensions on the relationship between the customer and the service provider. First, for integrity-based trust, I show a positive effect of early interactions with customer success teams on product usage. Second, I show that benevolence-based trust increases customer engagement. Third, I use supervised machine learning and explainability methods to illustrate the positive effect of the ABI trust dimensions on customer contract extensions. Methodologically, this work suggests a strategy for machine learning applications in sociological research. Finally, this work derives practical managerial implications for service providers

Automatic extraction of definitions

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2014This doctoral research work provides a set of methods and heuristics for building a definition extractor or for fine-tuning an existing one. In order to develop and test the architecture, a generic definitions extractor for the Portuguese language is built. Furthermore, the methods were tested in the construction of an extractor for two languages different from Portuguese, which are English and, less extensively, Dutch. The approach presented in this work makes the proposed extractor completely different in nature in comparison to the other works in the field. It is a matter of fact that most systems that automatically extract definitions have been constructed taking into account a specific corpus on a specific topic, and are based on the manual construction of a set of rules or patterns capable of identifyinf a definition in a text. This research focused on three types of definitions, characterized by the connector between the defined term and its description. The strategy adopted can be seen as a "divide and conquer"approach. Differently from the other works representing the state of the art, specific heuristics were developed in order to deal with different types of definitions, namely copula, verbal and punctuation definitions. We used different methodology for each type of definition, namely we propose to use rule-based methods to extract punctuation definitions, machine learning with sampling algorithms for copula definitions, and machine learning with a method to increase the number of positive examples for verbal definitions. This architecture is justified by the increasing linguistic complexity that characterizes the different types of definitions. Numerous experiments have led to the conclusion that the punctuation definitions are easily described using a set of rules. These rules can be easily adapted to the relevant context and translated into other languages. However, in order to deal with the other two definitions types, the exclusive use of rules is not enough to get good performance and it asks for more advanced methods, in particular a machine learning based approach. Unlike other similar systems, which were built having in mind a specific corpus or a specific domain, the one reported here is meant to obtain good results regardless the domain or context. All the decisions made in the construction of the definition extractor take into consideration this central objective.Este trabalho de doutoramento visa proporcionar um conjunto de métodos e heurísticas para a construção de um extractor de definição ou para melhorar o desempenho de um sistema já existente, quando usado com um corpus específico. A fim de desenvolver e testar a arquitectura, um extractor de definic˛ões genérico para a língua Portuguesa foi construído. Além disso, os métodos foram testados na construção de um extractor para um idioma diferente do Português, nomeadamente Inglês, algumas heurísticas também foram testadas com uma terceira língua, ou seja o Holandês. A abordagem apresentada neste trabalho torna o extractor proposto neste trabalho completamente diferente em comparação com os outros trabalhos na área. É um fato que a maioria dos sistemas de extracção automática de definicões foram construídos tendo em conta um corpus específico com um tema bem determinado e são baseados na construc˛ão manual de um conjunto de regras ou padrões capazes de identificar uma definição num texto dum domínio específico. Esta pesquisa centrou-se em três tipos de definições, caracterizadas pela ligacão entre o termo definido e a sua descrição. A estratégia adoptada pode ser vista como uma abordagem "dividir para conquistar". Diferentemente de outras pesquisa nesta área, foram desenvolvidas heurísticas específicas a fim de lidar com as diferentes tipologias de definições, ou seja, cópula, verbais e definicões de pontuação. No presente trabalho propõe-se utilizar uma metodologia diferente para cada tipo de definição, ou seja, propomos a utilização de métodos baseados em regras para extrair as definições de pontuação, aprendizagem automática, com algoritmos de amostragem para definições cópula e aprendizagem automática com um método para aumentar automáticamente o número de exemplos positivos para a definição verbal. Esta arquitetura é justificada pela complexidade linguística crescente que caracteriza os diferentes tipos de definições. Numerosas experiências levaram à conclusão de que as definições de pontuação são facilmente descritas utilizando um conjunto de regras. Essas regras podem ser facilmente adaptadas ao contexto relevante e traduzido para outras línguas. No entanto, a fim de lidar com os outros dois tipos de definições, o uso exclusivo de regras não é suficiente para obter um bom desempenho e é preciso usar métodos mais avançados, em particular aqueles baseados em aprendizado de máquina. Ao contrário de outros sistemas semelhantes, que foram construídos tendo em mente um corpus ou um domínio específico, o sistema aqui apresentado foi desenvolvido de maneira a obter bons resultados, independentemente do domínio ou da língua. Todas as decisões tomadas na construção do extractor de definição tiveram em consideração este objectivo central.Fundação para a Ciência e a Tecnologia (FCT, SFRH/ BD/36732/2007