KARL: A Knowledge-Assisted Retrieval Language

Data classification and storage are tasks typically performed by application specialists. In contrast, information users are primarily non-computer specialists who use information in their decision-making and other activities. Interaction efficiency between such users and the computer is often reduced by machine requirements and resulting user reluctance to use the system. This thesis examines the problems associated with information retrieval for non-computer specialist users, and proposes a method for communicating in restricted English that uses knowledge of the entities involved, relationships between entities, and basic English language syntax and semantics to translate the user requests into formal queries. The proposed method includes an intelligent dictionary, syntax and semantic verifiers, and a formal query generator. In addition, the proposed system has a learning capability that can improve portability and performance. With the increasing demand for efficient human-machine communication, the significance of this thesis becomes apparent. As human resources become more valuable, software systems that will assist in improving the human-machine interface will be needed and research addressing new solutions will be of utmost importance. This thesis presents an initial design and implementation as a foundation for further research and development into the emerging field of natural language database query systems

Anaphora Resolution and Text Retrieval

Empirical approaches based on qualitative or quantitative methods of corpus linguistics have become a central paradigm within linguistics. The series takes account of this fact and provides a platform for approaches within synchronous linguistics as well as interdisciplinary works with a linguistic focus which devise new ways of working empirically and develop new data-based methods and theoretical models for empirical linguistic analyses

Extraction of Problem Events from Web Documents to Construct Cause-Effect Loop

This research aims to extract problem events, particularly cause-effect concept pair series with explanations by several simple sentences with causative/effect concepts, from web documents of drug addiction. The extracted problem events are used to construct cause-effect loop which benefits for the problem analysis in the solving system. The research has three problems; how to determine the cause/effect event concepts expressed by verb phrases having a problem of the overlap between causative-verb concepts and effect-verb concepts, how to determine the series of cause-effect concept pairs with the causative/effect concept boundary consideration, and how to determine the feedback-loop of cause-effect concept pair series. Therefore, we apply the event rate to solve the overlap problem. We then propose using N-WordCo to determine the cause-effect concept pair series and also use a cue-word set to solve the feedback-loop. The research results provide the high precision of the problem event extraction from the documents

Machine Learning and the Cognitive Basis of Natural Language

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Anaphora Resolution and Text Retrieval

Empirical approaches based on qualitative or quantitative methods of corpus linguistics have become a central paradigm within linguistics. The series takes account of this fact and provides a platform for approaches within synchronous linguistics as well as interdisciplinary works with a linguistic focus which devise new ways of working empirically and develop new data-based methods and theoretical models for empirical linguistic analyses

Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models

Natural language often contains ambiguities that can lead to misinterpretation and miscommunication. While humans can handle ambiguities effectively by asking clarifying questions and/or relying on contextual cues and common-sense knowledge, resolving ambiguities can be notoriously hard for machines. In this work, we study ambiguities that arise in text-to-image generative models. We curate a benchmark dataset covering different types of ambiguities that occur in these systems. We then propose a framework to mitigate ambiguities in the prompts given to the systems by soliciting clarifications from the user. Through automatic and human evaluations, we show the effectiveness of our framework in generating more faithful images aligned with human intention in the presence of ambiguities