Ph.D. / Medical Informatics and Clinical Epidemiology / Tools to automatically summarize gene information from the literature have the potential to help genomics researchers better interpret gene expression data and investigate biological pathways. Even though several useful human-curated databases of information about genes already exist, these have significant limitations. First, their construction requires intensive human labor. Second, curation of genes lags behind the rapid publication rate of new research and discoveries. Finally, most of the curated knowledge is limited to information on single genes. As such, most original and up-to-date knowledge on genes can only be found in the immense amount of unstructured, free text biomedical literature. Genomic researchers frequently encounter the task of finding information on sets of differentially expressed genes from the results of common highthroughput technologies like microarray experiments. However, finding information on a set of genes by manually searching and scanning the literature is a time-consuming and daunting task for scientists. For example, PubMed, the first choice of literature research for biologists, usually returns hundreds of references for a search on a single gene in reverse chronological order. Therefore, a tool to summarize the available textual information on genes could be a valuable tool for scientists. In this study, we adapted automatic summarization technologies to the biomedical domain to build a query-based, task-specific automatic summarizer of information on mouse genes studied in microarray experiments - mouse Gene Information Clustering and Summarization System (GICSS). GICSS first clusters a set of differentially expressed genes by Medical Subject Heading (MeSH), Gene Ontology (GO), and free text features into functionally similar groups;next it presents summaries for each gene as ranked sentences extracted from MEDLINE abstracts, with the ranking emphasizing the relation between genes, similarity to the function cluster it belongs to, and recency. GICSS is available as a web application with links to the PubMed (www.pubmed.gov) website for each extracted sentence. It integrates two related steps, functional gene clustering and gene information gathering, of the microarray data analysis process. The information from the clustering step was used to construct the context for summarization. The evaluation of the system was conducted with scientists who were analyzing their real microarray datasets. The evaluation results showed that GICSS can provide meaningful clusters for real users in the genomic research area. In addition, the results also indicated that presenting sentences in the abstract can provide more important information to the user than just showing the title in the default PubMed format. Both domain-specific and non-domain-specific terminologies contributed in the informative sentences selection. Summarization may serve as a useful tool to help scientists to access information at the time of microarray data analysis. Further research includes setting up the automatic update of MEDLINE records; extending and fine-tuning of the feature parameters for sentence scoring using the available evaluation data; and expanding GICSS to incorporate textual information from other species. Finally, dissemination and integration of GICSS into the current workflow of the microarray analysis process will help to make GICSS a truly useful tool for the targeted users, biomedical genomics researchers.

Geschäftsprozesse im Verwaltungs- und Dienstleistungsbereich werden häufig durch den Eingang von Dokumenten angestoßen. Hierfür ist es unerlässlich, dass sie den richtigen Mitarbeiter im Unternehmen oder der Organisation erreichen. Oftmals sind jedoch dem externen Sender die internen Organisationsstrukturen nicht klar, so dass eine zentrale Stelle angeschrieben wird. Diese muss dann das Dokument, basierend auf seinem Inhalt, an die zuständigen Kollegen weiterleiten. Dies kann beträchtlichen personellen Aufwand mit sich bringen. In der Forschungsarbeit wird ein System entwickelt, das diese Aufgabe maschinell erfüllen soll. Hierzu werden verschiedenartige Klassifikationsverfahren erprobt und hinsichtlich ihrer Verlässlichkeit beurteilt. Weiterhin werden Verbesserungen gegenüber gängigen maschinellen Verfahren angestrebt.

We present a multifaceted investigation into the relevance of word order in machine translation. We introduce two tools, DTED and DERP, each using dependency structure to detect differences between the structures of machine-produced translations and human-produced references. DTED applies the principle of Tree Edit Distance to calculate edit operations required to convert one structure into another. Four variants of DTED have been produced, differing in the importance they place on words which match between the two sentences. DERP represents a more detailed procedure, making use of the dependency relations between words when evaluating the disparities between paths connecting matching nodes. In order to empirically evaluate DTED and DERP, and as a standalone contribution, we have produced WOJ-DB, a database of human judgments. Containing scores relating to translation adequacy and more specifically to word order quality, this is intended to support investigations into a wide range of translation phenomena. We report an internal evaluation of the information in WOJ-DB, then use it to evaluate variants of DTED and DERP, both to determine their relative merit and their strength relative to third-party baselines. We present our conclusions about the importance of structure to the tools and their relevance to word order specifically, then propose further related avenues of research suggested or enabled by our work.

Geschäftsprozesse im Verwaltungs- und Dienstleistungsbereich werden häufig durch den Eingang von Dokumenten angestoßen. Hierfür ist es unerlässlich, dass sie den richtigen Mitarbeiter im Unternehmen oder der Organisation erreichen. Oftmals sind jedoch dem externen Sender die internen Organisationsstrukturen nicht klar, so dass eine zentrale Stelle angeschrieben wird. Diese muss dann das Dokument, basierend auf seinem Inhalt, an die zuständigen Kollegen weiterleiten. Dies kann beträchtlichen personellen Aufwand mit sich bringen. In der Forschungsarbeit wird ein System entwickelt, das diese Aufgabe maschinell erfüllen soll. Hierzu werden verschiedenartige Klassifikationsverfahren erprobt und hinsichtlich ihrer Verlässlichkeit beurteilt. Weiterhin werden Verbesserungen gegenüber gängigen maschinellen Verfahren angestrebt.

The aim of the present study has been to examine users' experience of Chatbot from a business perspective and a consumer perspective. The study has also focused on highlighting what limitations a Chatbot can have and possible improvements for future development. The study is based on a qualitative research method with semi-structured interviews that have been analyzed on the basis of a thematic analysis. The results of the interview material have been analyzed based on previous research and various theoretical perspectives such as Artificial Intelligence (AI), Natural Language Processing (NLP). The results of the study have shown that the experience of Chatbot can differ between businesses that offer Chatbot, which are more positive and consumers who use it as customer service. Limitations and suggestions for improvements around Chatbotar are also a consistent result of the study. / Den föreliggande studie har haft som syfte att undersöka användarnas upplevelse av Chatbot utifrån verksamhetsperspektiv och konsumentperspektiv. Studien har också fokuserat på att lyfta fram vilka begränsningar en Chatbot kan ha och eventuella förbättringar för framtida utvecklingen. Studien är baserad på en kvalitativ forskningsmetod med semistrukturerade intervjuer som har analyserats utifrån en tematisk analys. Resultatet av intervjumaterialet har analyserat utifrån tidigare forskning och olika teoretiska perspektiv som Artificial Intelligence (AI), Natural Language Processing (NLP). Resultatet av studien har visat att upplevelsen av Chatbot kan skilja sig mellan verksamheter som erbjuder Chatbot, som är mer positiva och konsumenter som använder det som kundtjänst. Begränsningar och förslag på förbättringar kring Chatbotar är också ett genomgående resultat i studien.

<p> The dramatic improvement in information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. CourseNetworking is a next-generation LMS adopting machine learning to add personalization, gamification, and more dynamics to the system. This work tries to come up with two recommender systems that can help improve CourseNetworking services. The first one is a social recommender system helping CourseNetworking to track user interests and give more relevant recommendations. Recently, graph neural network (GNN) techniques have been employed in social recommender systems due to their high success in graph representation learning, including social network graphs. Despite the rapid advances in recommender systems performance, dealing with the dynamic property of the social network data is one of the key challenges that is remained to be addressed. In this research, a novel method is presented that provides social recommendations by incorporating the dynamic property of social network data in a heterogeneous graph by supplementing the graph with time span nodes that are used to define users long-term and short-term preferences over time. The second service that is proposed to add to Rumi services is a hashtag recommendation system that can help users label their posts quickly resulting in improved searchability of content. In recent years, several hashtag recommendation methods are proposed and developed to speed up processing of the texts and quickly find out the critical phrases. The methods use different approaches and techniques to obtain critical information from a large amount of data. This work investigates the efficiency of unsupervised keyword extraction methods for hashtag recommendation and recommends the one with the best performance to use in a hashtag recommender system. </p>

This work describes an approach to determine whether people participate in the events they tweet about. Specifically, we determine whether people are participants in events with respect to the tweet timestamp. We target all events expressed by verbs in tweets, including past, present and events that may occur in future. We define event participant as people directly involved in an event regardless of whether they are the agent, recipient or play another role. We present an annotation effort, guidelines and quality analysis with 1,096 event mentions. We discuss the label distributions and event behavior in the annotated corpus. We also explain several features used and a standard supervised machine learning approach to automatically determine if and when the author is a participant of the event in the tweet. We discuss trends in the results obtained and devise important conclusions.

Syntactic complexity has been an area of significant interest in L2 writing development studies over the past 45 years. Despite the regularity in which syntactic complexity measures have been employed, the construct is still relatively under-developed, and, as a result, the cumulative results of syntactic complexity studies can appear opaque. At least three reasons exist for the current state of affairs, namely the lack of consistency and clarity by which indices of syntactic complexity have been described, the overly broad nature of the indices that have been regularly employed, and the omission of indices that focus on usage-based perspectives. This study seeks to address these three gaps through the development and validation of the Tool for the Automatic Assessment of Syntactic Sophistication and Complexity (TAASSC). TAASSC measures large and fined grained clausal and phrasal indices of syntactic complexity and usage-based frequency/contingency indices of syntactic sophistication. Using TAASSC, this study will address L2 writing development in two main ways: through the examination of syntactic development longitudinally and through the examination of human judgments of writing proficiency (e.g., expert ratings of TOEFL essays). This study will have important implications for second language acquisition, second language writing, and language assessment.

Some well-studied infinite-state stochastic models give rise to systems of nonlinear equations. These systems of equations have solutions that are probabilities, generally probabilities of termination in the model. We are interested in finding efficient, preferably polynomial time, algorithms for calculating probabilities associated with these models. The chief tool we use to solve systems of polynomial equations will be Newton’s method as suggested by [EY09]. The main contribution of this thesis is to the analysis of this and related algorithms. We give polynomial-time algorithms for calculating probabilities for broad classes of models for which none were known before. Stochastic models that give rise to such systems of equations include such classic and heavily-studied models as Multi-type Branching Processes, Stochastic Context- Free Grammars(SCFGs) and Quasi Birth-Death Processes. We also consider models that give rise to infinite-state Markov Decision Processes (MDPs) by giving algorithms for approximating optimal probabilities and finding policies that give probabilities close to the optimal probability, in several classes of infinite-state MDPs. Our algorithms for analysing infinite-state MDPs rely on a non-trivial generalization of Newton’s method that works for the max/min polynomial systems that arise as Bellman optimality equations in these models. For SCFGs, which are used in statistical natural language processing, in addition to approximating termination probabilities, we analyse algorithms for approximating the probability that a grammar produces a given string, or produces a string in a given regular language. In most cases, we show that we can calculate an approximation to the relevant probability in time polynomial in the size of the model and the number of bits of desired precision. We also consider more general systems of monotone polynomial equations. For such systems we cannot give a polynomial-time algorithm, which pre-existing hardness results render unlikely, but we can still give an algorithm with a complexity upper bound which is exponential only in some parameters that are likely to be bounded for the monotone polynomial equations that arise for many interesting stochastic models.

Measures of classical rhetorical structure in text can improve accuracy in certain types of stylistic classification tasks such as authorship attribution. This research augments the relatively scarce work in the automated identification of rhetorical figures and uses the resulting statistics to characterize an author's rhetorical style. These characterizations of style can then become part of the feature set of various classification models. Our Rhetorica software identifies 14 classical rhetorical figures in free English text, with generally good precision and recall, and provides summary measures to use in descriptive or classification tasks. Classification models trained on Rhetorica's rhetorical measures paired with lexical features typically performed better at authorship attribution than either set of features used individually. The rhetorical measures also provide new stylistic quantities for describing texts, authors, genres, etc.