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(1 - 7 of 7)
- Title
- Machine Learning from Casual Conversation.
- Creator
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Mohammed Ali, Awrad, Sukthankar, Gita, Wu, Annie, Boloni, Ladislau, University of Central Florida
- Abstract / Description
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Human social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. In this dissertation, we introduce another form of social learning, Learning from a Casual Conversation (LCC). LCC is an open-ended machine learning system in which an artificially intelligent agent learns from an extended dialog with a human. Our system enables the agent to incorporate changes into its knowledge base,...
Show moreHuman social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. In this dissertation, we introduce another form of social learning, Learning from a Casual Conversation (LCC). LCC is an open-ended machine learning system in which an artificially intelligent agent learns from an extended dialog with a human. Our system enables the agent to incorporate changes into its knowledge base, based on the human's conversational text input. This system emulates how humans learn from each other through a dialog. LCC closes the gap in the current research that is focused on teaching specific tasks to computer agents. Furthermore, LCC aims to provide an easy way to enhance the knowledge of the system without requiring the involvement of a programmer. This system does not require the user to enter specific information; instead, the user can chat naturally with the agent. LCC identifies the inputs that contain information relevant to its knowledge base in the learning process. LCC's architecture consists of multiple sub-systems combined to perform the task. Its learning component can add new knowledge to existing information in the knowledge base, confirm existing information, and/or update existing information found to be related to the user input. %The test results indicate that the prototype was successful in learning from a conversation. The LCC system functionality was assessed using different evaluation methods. This includes tests performed by the developer, as well as by 130 human test subjects. Thirty of those test subjects interacted directly with the system and completed a survey of 13 questions/statements that asked the user about his/her experience using LCC. A second group of 100 human test subjects evaluated the dialogue logs of a subset of the first group of human testers. The collected results were all found to be acceptable and within the range of our expectations.
Show less - Date Issued
- 2019
- Identifier
- CFE0007503, ucf:52634
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007503
- Title
- The Hermeneutics of the Hard Drive: Using Narratology, Natural Language Processing, and Knowledge Management to Improve the Effectiveness of the Digital Forensic Process.
- Creator
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Pollitt, Mark, Applen, John, Bowdon, Melody, Dombrowski, Paul, Craiger, John, University of Central Florida
- Abstract / Description
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In order to protect the safety of our citizens and to ensure a civil society, we ask our law enforcement, judiciary and intelligence agencies, under the rule of law, to seek probative information which can be acted upon for the common good. This information may be used in court to prosecute criminals or it can be used to conduct offensive or defensive operations to protect our national security. As the citizens of the world store more and more information in digital form, and as they live an...
Show moreIn order to protect the safety of our citizens and to ensure a civil society, we ask our law enforcement, judiciary and intelligence agencies, under the rule of law, to seek probative information which can be acted upon for the common good. This information may be used in court to prosecute criminals or it can be used to conduct offensive or defensive operations to protect our national security. As the citizens of the world store more and more information in digital form, and as they live an ever-greater portion of their lives online, law enforcement, the judiciary and the Intelligence Community will continue to struggle with finding, extracting and understanding the data stored on computers. But this trend affords greater opportunity for law enforcement. This dissertation describes how several disparate approaches: knowledge management, content analysis, narratology, and natural language processing, can be combined in an interdisciplinary way to positively impact the growing difficulty of developing useful, actionable intelligence from the ever-increasing corpus of digital evidence. After exploring how these techniques might apply to the digital forensic process, I will suggest two new theoretical constructs, the Hermeneutic Theory of Digital Forensics and the Narrative Theory of Digital Forensics, linking existing theories of forensic science, knowledge management, content analysis, narratology, and natural language processing together in order to identify and extract narratives from digital evidence. An experimental approach will be described and prototyped. The results of these experiments demonstrate the potential of natural language processing techniques to digital forensics.
Show less - Date Issued
- 2013
- Identifier
- CFE0005112, ucf:50749
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005112
- Title
- SYNTAX-BASED CONCEPT EXTRACTION FOR QUESTION ANSWERING.
- Creator
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Glinos, Demetrios, Gomez, Fernando, University of Central Florida
- Abstract / Description
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Question answering (QA) stands squarely along the path from document retrieval to text understanding. As an area of research interest, it serves as a proving ground where strategies for document processing, knowledge representation, question analysis, and answer extraction may be evaluated in real world information extraction contexts. The task is to go beyond the representation of text documents as "bags of words" or data blobs that can be scanned for keyword combinations and word...
Show moreQuestion answering (QA) stands squarely along the path from document retrieval to text understanding. As an area of research interest, it serves as a proving ground where strategies for document processing, knowledge representation, question analysis, and answer extraction may be evaluated in real world information extraction contexts. The task is to go beyond the representation of text documents as "bags of words" or data blobs that can be scanned for keyword combinations and word collocations in the manner of internet search engines. Instead, the goal is to recognize and extract the semantic content of the text, and to organize it in a manner that supports reasoning about the concepts represented. The issue presented is how to obtain and query such a structure without either a predefined set of concepts or a predefined set of relationships among concepts. This research investigates a means for acquiring from text documents both the underlying concepts and their interrelationships. Specifically, a syntax-based formalism for representing atomic propositions that are extracted from text documents is presented, together with a method for constructing a network of concept nodes for indexing such logical forms based on the discourse entities they contain. It is shown that meaningful questions can be decomposed into Boolean combinations of question patterns using the same formalism, with free variables representing the desired answers. It is further shown that this formalism can be used for robust question answering using the concept network and WordNet synonym, hypernym, hyponym, and antonym relationships. This formalism was implemented in the Semantic Extractor (SEMEX) research tool and was tested against the factoid questions from the 2005 Text Retrieval Conference (TREC), which operated upon the AQUAINT corpus of newswire documents. After adjusting for the limitations of the tool and the document set, correct answers were found for approximately fifty percent of the questions analyzed, which compares favorably with other question answering systems.
Show less - Date Issued
- 2006
- Identifier
- CFE0000985, ucf:46711
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000985
- Title
- THE ACQUISITION OF LEXICAL KNOWLEDGE FROM THE WEB FOR ASPECTS OF SEMANTIC INTERPRETATION.
- Creator
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Schwartz, Hansen, Gomez, Fernando, University of Central Florida
- Abstract / Description
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This work investigates the effective acquisition of lexical knowledge from the Web to perform semantic interpretation. The Web provides an unprecedented amount of natural language from which to gain knowledge useful for semantic interpretation. The knowledge acquired is described as common sense knowledge, information one uses in his or her daily life to understand language and perception. Novel approaches are presented for both the acquisition of this knowledge and use of the knowledge in...
Show moreThis work investigates the effective acquisition of lexical knowledge from the Web to perform semantic interpretation. The Web provides an unprecedented amount of natural language from which to gain knowledge useful for semantic interpretation. The knowledge acquired is described as common sense knowledge, information one uses in his or her daily life to understand language and perception. Novel approaches are presented for both the acquisition of this knowledge and use of the knowledge in semantic interpretation algorithms. The goal is to increase accuracy over other automatic semantic interpretation systems, and in turn enable stronger real world applications such as machine translation, advanced Web search, sentiment analysis, and question answering. The major contributions of this dissertation consist of two methods of acquiring lexical knowledge from the Web, namely a database of common sense knowledge and Web selectors. The first method is a framework for acquiring a database of concept relationships. To acquire this knowledge, relationships between nouns are found on the Web and analyzed over WordNet using information-theory, producing information about concepts rather than ambiguous words. For the second contribution, words called Web selectors are retrieved which take the place of an instance of a target word in its local context. The selectors serve for the system to learn the types of concepts that the sense of a target word should be similar. Web selectors are acquired dynamically as part of a semantic interpretation algorithm, while the relationships in the database are useful to stand-alone programs. A final contribution of this dissertation concerns a novel semantic similarity measure and an evaluation of similarity and relatedness measures on tasks of concept similarity. Such tasks are useful when applying acquired knowledge to semantic interpretation. Applications to word sense disambiguation, an aspect of semantic interpretation, are used to evaluate the contributions. Disambiguation systems which utilize semantically annotated training data are considered supervised. The algorithms of this dissertation are considered minimally-supervised; they do not require training data created by humans, though they may use human-created data sources. In the case of evaluating a database of common sense knowledge, integrating the knowledge into an existing minimally-supervised disambiguation system significantly improved results -- a 20.5\% error reduction. Similarly, the Web selectors disambiguation system, which acquires knowledge directly as part of the algorithm, achieved results comparable with top minimally-supervised systems, an F-score of 80.2\% on a standard noun disambiguation task. This work enables the study of many subsequent related tasks for improving semantic interpretation and its application to real-world technologies. Other aspects of semantic interpretation, such as semantic role labeling could utilize the same methods presented here for word sense disambiguation. As the Web continues to grow, the capabilities of the systems in this dissertation are expected to increase. Although the Web selectors system achieves great results, a study in this dissertation shows likely improvements from acquiring more data. Furthermore, the methods for acquiring a database of common sense knowledge could be applied in a more exhaustive fashion for other types of common sense knowledge. Finally, perhaps the greatest benefits from this work will come from the enabling of real world technologies that utilize semantic interpretation.
Show less - Date Issued
- 2011
- Identifier
- CFE0003688, ucf:48805
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003688
- Title
- EPISODIC MEMORY MODEL FOR EMBODIED CONVERSATIONAL AGENTS.
- Creator
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Elvir, Miguel, Gonzalez, Avelino, University of Central Florida
- Abstract / Description
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Embodied Conversational Agents (ECA) form part of a range of virtual characters whose intended purpose include engaging in natural conversations with human users. While works in literature are ripe with descriptions of attempts at producing viable ECA architectures, few authors have addressed the role of episodic memory models in conversational agents. This form of memory, which provides a sense of autobiographic record-keeping in humans, has only recently been peripherally integrated into...
Show moreEmbodied Conversational Agents (ECA) form part of a range of virtual characters whose intended purpose include engaging in natural conversations with human users. While works in literature are ripe with descriptions of attempts at producing viable ECA architectures, few authors have addressed the role of episodic memory models in conversational agents. This form of memory, which provides a sense of autobiographic record-keeping in humans, has only recently been peripherally integrated into dialog management tools for ECAs. In our work, we propose to take a closer look at the shared characteristics of episodic memory models in recent examples from the field. Additionally, we propose several enhancements to these existing models through a unified episodic memory model for ECAÂÂ's. As part of our research into episodic memory models, we present a process for determining the prevalent contexts in the conversations obtained from the aforementioned interactions. The process presented demonstrates the use of statistical and machine learning services, as well as Natural Language Processing techniques to extract relevant snippets from conversations. Finally, mechanisms to store, retrieve, and recall episodes from previous conversations are discussed. A primary contribution of this research is in the context of contemporary memory models for conversational agents and cognitive architectures. To the best of our knowledge, this is the first attempt at providing a comparative summary of existing works. As implementations of ECAs become more complex and encompass more realistic conversation engines, we expect that episodic memory models will continue to evolve and further enhance the naturalness of conversations.
Show less - Date Issued
- 2010
- Identifier
- CFE0003353, ucf:48443
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003353
- Title
- Larger-first partial parsing.
- Creator
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Van Delden, Sebastian Alexander, Gomez, Fernando, Engineering and Computer Science
- Abstract / Description
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University of Central Florida College of Engineering Thesis; Larger-first partial parsing is a primarily top-down approach to partial parsing that is opposite to current easy-first, or primarily bottom-up, strategies. A rich partial tree structure is captured by an algorithm that assigns a hierarchy of structural tags to each of the input tokens in a sentence. Part-of-speech tags are first assigned to the words in a sentence by a part-of-speech tagger. A cascade of Deterministic Finite State...
Show moreUniversity of Central Florida College of Engineering Thesis; Larger-first partial parsing is a primarily top-down approach to partial parsing that is opposite to current easy-first, or primarily bottom-up, strategies. A rich partial tree structure is captured by an algorithm that assigns a hierarchy of structural tags to each of the input tokens in a sentence. Part-of-speech tags are first assigned to the words in a sentence by a part-of-speech tagger. A cascade of Deterministic Finite State Automata then uses this part-of-speech information to identify syntactic relations primarily ina descending order of their size. The cascade is divided into four specialized sections: (1) a Comma Network, which identifies syntactic relations associated with commas; (2) a Conjunction Network, which partially disambiguates phrasal conjunctions and fully disambiguates clausal conjunctions; (3) a Clause Network, which identifies non-comma-delimited clauses; and (4) a Phrase Network, which identifies the remaining base phrases in the sentence. Each automaton is capable of adding one ore more levels of structural tags to the to the tokens in a sentence. The larger-first approach is compared against a well-known easy-first approach. The results indicate that this larger-first approach is capable of (1) producing a more detailed partial parse than an easy first approach; (2) providing better containment of attachment ambiguity; (3) handling overlapping syntactic relations; and (4) achieving a higher accuracy than the easy-first approach. The automata of each network were developed by an empirical analysis of several sources and are presented here in details.
Show less - Date Issued
- 2003
- Identifier
- CFR0000760, ucf:52932
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFR0000760
- Title
- Automatically Acquiring a Semantic Network of Related Concepts.
- Creator
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Szumlanski, Sean, Gomez, Fernando, Wu, Annie, Hughes, Charles, Sims, Valerie, University of Central Florida
- Abstract / Description
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We describe the automatic acquisition of a semantic network in which over 7,500 of the most frequently occurring nouns in the English language are linked to their semantically related concepts in the WordNet noun ontology. Relatedness between nouns is discovered automatically from lexical co-occurrence in Wikipedia texts using a novel adaptation of an information theoretic inspired measure. Our algorithm then capitalizes on salient sense clustering among these semantic associates to...
Show moreWe describe the automatic acquisition of a semantic network in which over 7,500 of the most frequently occurring nouns in the English language are linked to their semantically related concepts in the WordNet noun ontology. Relatedness between nouns is discovered automatically from lexical co-occurrence in Wikipedia texts using a novel adaptation of an information theoretic inspired measure. Our algorithm then capitalizes on salient sense clustering among these semantic associates to automatically disambiguate them to their corresponding WordNet noun senses (i.e., concepts). The resultant concept-to-concept associations, stemming from 7,593 target nouns, with 17,104 distinct senses among them, constitute a large-scale semantic network with 208,832 undirected edges between related concepts. Our work can thus be conceived of as augmenting the WordNet noun ontology with RelatedTo links.The network, which we refer to as the Szumlanski-Gomez Network (SGN), has been subjected to a variety of evaluative measures, including manual inspection by human judges and quantitative comparison to gold standard data for semantic relatedness measurements. We have also evaluated the network's performance in an applied setting on a word sense disambiguation (WSD) task in which the network served as a knowledge source for established graph-based spreading activation algorithms, and have shown: a) the network is competitive with WordNet when used as a stand-alone knowledge source for WSD, b) combining our network with WordNet achieves disambiguation results that exceed the performance of either resource individually, and c) our network outperforms a similar resource, WordNet++ (Ponzetto (&) Navigli, 2010), that has been automatically derived from annotations in the Wikipedia corpus.Finally, we present a study on human perceptions of relatedness. In our study, we elicited quantitative evaluations of semantic relatedness from human subjects using a variation of the classical methodology that Rubenstein and Goodenough (1965) employed to investigate human perceptions of semantic similarity. Judgments from individual subjects in our study exhibit high average correlation to the elicited relatedness means using leave-one-out sampling (r = 0.77, ? = 0.09, N = 73), although not as high as average human correlation in previous studies of similarity judgments, for which Resnik (1995) established an upper bound of r = 0.90 (? = 0.07, N = 10). These results suggest that human perceptions of relatedness are less strictly constrained than evaluations of similarity, and establish a clearer expectation for what constitutes human-like performance by a computational measure of semantic relatedness. We also contrast the performance of a variety of similarity and relatedness measures on our dataset to their performance on similarity norms and introduce our own dataset as a supplementary evaluative standard for relatedness measures.
Show less - Date Issued
- 2013
- Identifier
- CFE0004759, ucf:49767
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004759
