In recent years the amount of information on the web has increased dramatically. As a result, it has become a challenge for the researchers to find effective ways that can help us query and extract meaning from these large repositories. Standard document search engines try to address the problem by presenting the users a ranked list of relevant documents. In most cases, this is not enough as the end-user has to go through the entire document to find out the answer he is looking for. Question answering, which is the retrieving of answers to natural language questions from a document collection, tries to remove the onus on the end-user by providing direct access to relevant information. This thesis is concerned with open-domain complex question answering. Unlike simple questions, complex questions cannot be answered easily as they often require inferencing and synthesizing information from multiple documents. Hence, we considered the task of complex question answering as query-focused multi-document summarization. In this thesis, to improve complex question answering we experimented with both empirical and machine learning approaches. We extracted several features of different types (i.e. lexical, lexical semantic, syntactic and semantic) for each of the sentences in the document collection in order to measure its relevancy to the user query. We have formulated the task of complex question answering using reinforcement framework, which to our best knowledge has not been applied for this task before and has the potential to improve itself by fine-tuning the feature weights from user feedback. We have also used unsupervised machine learning techniques (random walk, manifold ranking) and augmented semantic and syntactic information to improve them. Finally we experimented with question decomposition where instead of trying to find the answer of the complex question directly, we decomposed the complex question into a set of simple questions and synthesized the answers to get our final result. / x, 128 leaves : ill. ; 29 cm

Indiana University-Purdue University Indianapolis (IUPUI) / Pharmacodynamics (PD) studies the relationship between drug concentration and drug effect on target sites. This field has recently gained attention as studies involving PD Drug-Drug interactions (DDI) assure discovery of multi-targeted drug agents and novel efficacious drug combinations. A PD drug combination could be synergistic, additive or antagonistic depending upon the summed effect of the drug combination at a target site. The PD literature has grown immensely and most of its knowledge is dispersed across different scientific journals, thus the manual identification of PD DDI is a challenge. In order to support an automated means to extract PD DDI, we propose Pharmacodynamics Miner (PD-Miner). PD-Miner is a text-mining tool, which is capable of identifying PD DDI from in vitro PD experiments. It is powered by two major features, i.e., collection of full text articles and in vitro PD ontology. The in vitro PD ontology currently has four classes and more than hundred subclasses; based on these classes and subclasses the full text corpus is annotated. The annotated full text corpus forms a database of articles, which can be queried based upon drug keywords and ontology subclasses. Since the ontology covers term and concept meanings, the system is capable of formulating semantic queries. PD-Miner extracts in vitro PD DDI based upon references to cell lines and cell phenotypes. The results are in the form of fragments of sentences in which important concepts are visually highlighted. To determine the accuracy of the system, we used a gold standard of 5 expert curated articles. PD-Miner identified DDI with a recall of 75% and a precision of 46.55%. Along with the development of PD Miner, we also report development of a semantically annotated in vitro PD corpus. This corpus includes term and sentence level annotations and serves as a gold standard for future text mining.