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abstract: ABSTRACT The cold and the flu are two of the most prevalent diseases in the world. Many over the counter (OTC) medications have been created to combat the symptoms of these illnesses. Some medications take a holistic approach by claiming to alleviate a wide range of symptoms, while others target a specific symptom. As these medications become more ubiquitous within the United State of America (USA), consumers form associations and mental models about the cold/flu field. The goal of Study 1 was to build a Pathfinder network based on the associations consumers make between cold/flu symptoms and medications. 100 participants, 18 years or older, fluent in English, and residing in the USA, completed a survey about the relatedness of cold/flu symptoms to OTC medications. They rated the relatedness on a scale of 1 (highly unrelated) to 7 (highly related) and those rankings were used to build a Pathfinder network that represented the average of those associations. Study 2 was conducted to validate the Pathfinder network. A different set of 90 participants with the same restrictions as those in Study 1 completed a matching associations test. They were prompted to match symptoms and medications they associated closely with each other. Results showered a significant negative correlation between the geodetic distance (the number of links between objects in the Pathfinder network) separating symptoms and medications and frequency of pairing symptoms with medication. This provides evidence of the validity of the Pathfinder network. It was also seen that, higher the relatedness rating between symptoms and medications in Study 1, higher the frequency of pairing symptom to medication in Study 2, and the more directly linked those symptoms and medications were in the Pathfinder network. This network can inform pharmaceutical companies about which symptoms they most closely associate with, who their competitors are, what symptoms they can dominate, and how to market their medications more effectively. / Dissertation/Thesis / Masters Thesis Engineering 2020

Syntactic parsing is one of the best understood language processing applications. Since language and grammar have been formally defined, it is easy for computers to parse the syntactic structure of natural language text. Does meaning have structure as well? If it has, how can we analyze the structure? Previous systems rely on a one-to-one correspondence between syntactic rules and semantic rules. But such systems can only be applied to limited fragments of English. In this thesis, we propose a general-purpose shallow semantic parser which utilizes a semantic network (WordNet), and a frame dataset (FrameNet). Semantic relations recognized by the parser are based on how human beings represent knowledge of the world. Parsing semantic structure allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.

Neste trabalho discuto sobre o senso comum, que é invariavelmente uma dimensão social do conhecimento, concebido na forma de crenças, conhecimentos, julgamentos e valores que são igualmente compartilhados por um conjunto de pessoas. Por isso, concepções sobre o senso comum estão presentes explicitamente em diferentes disciplinas, como as Ciências Humanas e a Inteligência Artificial, e implicitamente em alguns conceitos fundamentais como o de inteligência. Meu objetivo neste trabalho é representar o senso comum. A tática empregada para isso foi questionar as diferenças existentes no entendimento entre as partes envolvidas em situações de entendimento comum. Diante disso, defendo que concepções abstratas e sistemáticas sobre o senso comum não conseguem explicar como pode haver, em uma dada situação, diferenças existentes no entendimento comum nem entendimento comum em meio a diferenças existentes. Alternativamente, defendo que o senso comum acontece localmente, de maneira não garantida e não sistemática. Como as palavras e conceitos no início das interações são ambíguas, polissêmicas, lacunosas, etc. há a necessidade de estabelecer referências comuns para que seja possível o entendimento comum e manter relações sociais mais duradouras: a) essas relações têm restrições sobre o número de relações mais próximas segundo a hipótese do cérebro social, e b) essas referências estão baseadas na relação de analogias de outras experiências como um modo de entender situações novas. As relações podem ser representadas na forma de redes sociais, enquanto as referências podem ser representadas por redes semânticas, nas quais a familiaridade do repertório pode ser tratada como o crescimento da rede total de referências de uma pessoa. Defendo que é possível conceber o paralelismo entre a rede social de uma pessoa e sua rede semântica geral, e esse paralelismo pode ser representado. / I discuss here the common sense, which is invariably a social dimension of knowledge, conceived in the form of beliefs, knowledge, judgments and values that are equally shared by a set of people. Hence, conceptions about common sense are explicitly present in different disciplines, such as the Human Sciences and Artificial Intelligence, and implicitly in some fundamental concepts such as intelligence. My goal here is to represent common sense. For that, I question the differences existing in the understanding between people involved in situations of common understanding. Therefore, I argue that abstract and systematic conceptions of common sense cannot explain how there can be \"differences in the common understanding\" or \"common understanding in the midst of existing differences\" in a given situation. Alternatively, I argue that common sense happens locally, in a not taken-for-grated and unsystematic way. Since at the beginning interactions words and concepts are both ambiguous, polysemic, lacunose, etc. there is a need to establish common references so that common understanding can be possible and more lasting social relations can be maintained: a) relations have restrictions on the number of closer ones according to the social brain hypothesis, and b) references are based on the relation of analogies of other experiences as a way to understand new situations. Relationships can be represented in the form of social networks, while references can be represented by semantic networks in which repertory familiarity can be treated as the growth of a person\'s total network of references. I argue that it is possible to conceive the parallelism between a person\'s social network and its general semantic network, and this parallelism can be represented.

In this study an outline is presented of the semantic network of the preposition up in American English in sentences extracted from the Corpus of Contemporary American English (COCA), which was done to determine what the most common uses and meanings of the preposition are, as well as to determine if most of its possible meanings are concrete or abstract. The results show that there is a salient use and also prototypical meaning of up, and that these are major factors that impact the semantic network of the preposition. This study was designed to be a source of information for EFL students who struggle to understand how prepositions function in the English language, and also what the prepositions can actually represent in a sentence. Concomitantly, the goal is to give information about the preposition up in a way that will allow students to analyze other prepositions and perhaps even other word classes.

The words in a language not only help us to construct the sentences but also contain some other features, which we usually underestimate. Each word relates itself to the remaining ones in some way. In our daily lives, we extensively use these relations in many areas, where question direction is also one of them. In this work, it is investigated whether the relations between the words can be useful for question direction and an approach for question direction is presented. Besides, a tool is devised in the way of this approach for a course given in Turkish. The relations between the words are represented by a semantic network for nouns and verbs. By passing through the whole course material and using the relations meronymy for only nouns / synonymy, antonymy, hypernymy, coordinated words for both nouns and verbs / entailment and causality for only verbs, the semantic network, which is the backbone of the application, is constructed. The end product of our research consists of three modules: &middot / getting the question from the user and constructing the set of words that are related to the words that make up the question &middot / scoring each course section by comparing the words of the question set and the words in the section &middot / presenting the sections that may contain the answer The sections that are evaluated are taken as the sections of the course for granted. The chat logs that expand three years of the course were taken by permission and questions were extracted from them. They were used for testing purposes of the constructed application.

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 113-124).

Neste trabalho discuto sobre o senso comum, que é invariavelmente uma dimensão social do conhecimento, concebido na forma de crenças, conhecimentos, julgamentos e valores que são igualmente compartilhados por um conjunto de pessoas. Por isso, concepções sobre o senso comum estão presentes explicitamente em diferentes disciplinas, como as Ciências Humanas e a Inteligência Artificial, e implicitamente em alguns conceitos fundamentais como o de inteligência. Meu objetivo neste trabalho é representar o senso comum. A tática empregada para isso foi questionar as diferenças existentes no entendimento entre as partes envolvidas em situações de entendimento comum. Diante disso, defendo que concepções abstratas e sistemáticas sobre o senso comum não conseguem explicar como pode haver, em uma dada situação, diferenças existentes no entendimento comum nem entendimento comum em meio a diferenças existentes. Alternativamente, defendo que o senso comum acontece localmente, de maneira não garantida e não sistemática. Como as palavras e conceitos no início das interações são ambíguas, polissêmicas, lacunosas, etc. há a necessidade de estabelecer referências comuns para que seja possível o entendimento comum e manter relações sociais mais duradouras: a) essas relações têm restrições sobre o número de relações mais próximas segundo a hipótese do cérebro social, e b) essas referências estão baseadas na relação de analogias de outras experiências como um modo de entender situações novas. As relações podem ser representadas na forma de redes sociais, enquanto as referências podem ser representadas por redes semânticas, nas quais a familiaridade do repertório pode ser tratada como o crescimento da rede total de referências de uma pessoa. Defendo que é possível conceber o paralelismo entre a rede social de uma pessoa e sua rede semântica geral, e esse paralelismo pode ser representado. / I discuss here the common sense, which is invariably a social dimension of knowledge, conceived in the form of beliefs, knowledge, judgments and values that are equally shared by a set of people. Hence, conceptions about common sense are explicitly present in different disciplines, such as the Human Sciences and Artificial Intelligence, and implicitly in some fundamental concepts such as intelligence. My goal here is to represent common sense. For that, I question the differences existing in the understanding between people involved in situations of common understanding. Therefore, I argue that abstract and systematic conceptions of common sense cannot explain how there can be \"differences in the common understanding\" or \"common understanding in the midst of existing differences\" in a given situation. Alternatively, I argue that common sense happens locally, in a not taken-for-grated and unsystematic way. Since at the beginning interactions words and concepts are both ambiguous, polysemic, lacunose, etc. there is a need to establish common references so that common understanding can be possible and more lasting social relations can be maintained: a) relations have restrictions on the number of closer ones according to the social brain hypothesis, and b) references are based on the relation of analogies of other experiences as a way to understand new situations. Relationships can be represented in the form of social networks, while references can be represented by semantic networks in which repertory familiarity can be treated as the growth of a person\'s total network of references. I argue that it is possible to conceive the parallelism between a person\'s social network and its general semantic network, and this parallelism can be represented.

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.

Web surveys in higher education are particularly important for evaluating the quality of academic teaching and learning. Traditionally, mainly quantitative data is used for quality assessment. Increasingly, questions are being raised about the impact of attitudes of individuals involved. Therefore, especially the analysis of open-ended text responses in web surveys offers the potential for impact evaluation. Despite the fact that qualitative text mining, sentiment analysis, and network analytics are being introduced in other research areas, these instruments are still slowly gaining access to evaluation research. On the one hand, there is a lack of methodological expertise to deal with large numbers of text responses (e.g. via semantic analysis, linguistically supported coding, etc.). On the other hand, deficiencies in interdisciplinary expertise are identified in order to be able to contextualize the results. The contribution contributes to the field of impact evaluation and reveals methodological implications for the development of text mining, sentiment analysis, and network analytics in evaluation processes.