Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 238 for “"information extraction"”.
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Information Extraction from Scientific Literature
… bottleneck across research communities. Manual extraction of critical information---including methodologies, datasets, and domain-specific terminologies---now consumes a substantial proportion of researchers' literature review time, particularly impacting time-sensitive fields like climate …
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Information extraction with weak supervision
… to address key challenges in three fundamental information extraction (IE) tasks: Named Entity Recognition (NER), Relation Extraction (RE), and Entity Linking (EL). Traditional supervised learning methods in these domains often require extensive human annotations, which are costly and …
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Challenges in Managing Information Extraction
In this dissertation, we develop solutions to the key challenges mentioned above. First, we develop a declarative framework that can help make it easier for developers to write and understand IE programs, and show how to automatically optimize IE programs written in this framework to reduce …
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Machine Learning for Information Extraction
… machine learning techniques and applies them to information extraction. The study addresses several information extraction subtasks: part of speech tagging, entity extraction, coreference resolution, and relation extraction. Each of the tasks is formalized as a learning problem and appropriate …
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Information extraction for clinical narratives
… Records (EHRs). These EHRs contain valuable information which can be used in Clinical Decision Support (CDS). So, Information Extraction (IE) from EHRs is a very promising research area. In this thesis, I focus on two tasks namely Mention Detection and Coreference Resolution. A lot of domain …
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Joint document-level information extraction
… been used to great effect in order to solve many information extraction tasks. However, there are still many challenges that need to be solved before our models can achieve a level of natural language understanding that could be comparable to human. In order to accomplish that, we need to create …
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Cold-start universal information extraction
… The answers to these questions underpin the key information conveyed in the overwhelming majority, if not all, of language-based communication. At the core of my research in Information Extraction (IE) is the desire to endow machines with the ability to automatically extract, assess, and …
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Information extraction from chemical patents
The automated extraction of semantic chemical data from the existing literature is demonstrated. For reasons of copyright, the work is focused on the patent literature, though the methods are expected to apply equally to other areas of the chemical literature. Hearst Patterns are applied to the …
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Structural and semantic information extraction
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
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Information extraction with neural networks
… we present an ANN architecture for relation extraction, which ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C).
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Learning and Inference for Information Extraction
… frameworks have been applied to a variety of information extraction tasks, including entity extraction, entity/relation recognition, and semantic role labeling.
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Multilingual multitask joint neural information extraction
In the age of information overload, the ability to automatically extract useful structured information from texts is urgently needed by a wide range of applications, such as information retrieval and question answering. Over the past decades, researchers have proposed various Information Extraction …
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Efficient Information Extraction Using Statistical Relational Learning
Information extraction has gained significant importance due to the dramatic increase of information stored in the form of natural language text. In this thesis we explore a machine learning-based approach to support a natural language processing (NLP) algorithm, and an application of information …
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Scalable information extraction with large language models
Information extraction (IE) transforms unstructured text into structured knowledge such as entities and relations, and is fundamental to applications including knowledge graph construction, information retrieval, question answering, and domain-specific document understanding. Although large …
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A Linguistic Approach to Automatic Information Extraction
Contains fulltext : mmubn000001_220849838.pdf (Publisher’s version ) (Open Access)
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Temporal Information Extraction and Knowledge Base Population
<p>Temporal Information Extraction (TIE) from text plays an important role in many Natural Language Processing and Database applications. Many features of the world are time-dependent, and rich temporal knowledge is required for a more complete and precise understanding of the world. In this thesis …
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A Hybrid Approach to General Information Extraction
<p>Information Extraction (IE) is the process of analyzing documents and identifying desired pieces of information within them. Many IE systems have been developed over the last couple of decades, but there is still room for improvement as IE remains an open problem for researchers. This work …
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Information extraction and integration in mineral exploration.
Geologic information extraction and integration are the main goals of this study. Tools are designed to aid in exploration for common mineral deposits by intelligently and efficiently processing spatial geological data. Gabor filters, comprising Gaussian-attenuated sinusoidal weight vectors, are …
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Semantic pattern discovery in open information extraction
Open information extraction (OpenIE) is a novel paradigm that produces structured information from unstructured text with minimum or no supervision. The task involves extracting relevant relation tuples or expressions from a text corpus. Existing methods in the domain tend to produce a large …
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