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 13 of 13 for “"Entity Extraction"”.
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Cross-lingual entity extraction and linking for 300 languages
The student, Xiaoman Pan, accepted the attached license on 2020-12-02 at 17:38.
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Machine Learning for Information Extraction
… 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 learning …
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Learning and Inference for Information Extraction
… have been applied to a variety of information extraction tasks, including entity extraction, entity/relation recognition, and semantic role labeling.
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Building and processing a dataset containing articles related to food adulteration
… detection, query reforumulation, and entity extraction and text deduplication. All proposed algorithms were implemented in Python, and performance was evaluated on multiple datasets. Methods described in this thesis can be generalized to other applications as well.
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Extracting information from informal communication
… for sub-problems of the information extraction task. We make contributions to the problems of named entity extraction, co-reference resolution and context tracking. We channel our efforts toward methods which are particularly applicable to informal communication. We also consider a …
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Extracting fields from free-text
The Field Extraction Library (FEL) provides functions for named-entity extraction within free text. FEL models the content structure of the specified named-entities rather than relying on brittle, context-specific separator logic. Users specify the names of the fields they wish to extract, which …
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Knowledge extraction from unstructured data and classification through distributed ontologies
… mainly consists in three steps: \emph{i)} the extraction from the text of atomic pieces of information, called named entities; \emph{ii)} the classification of these pieces of information through ontologies; \emph{iii)} the disambigation of them through Uniform Resource Identifiers (URIs) …
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Entity-based long document summarization using LLMs
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
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NOVEL DATA MINING ALGORITHMS FOR ANALYSIS OF ELECTRONIC HEALTH RECORDS
… phenotyping, health risk prediction, and medical entity extraction. In this thesis, we focus on several important issues: (1) how to convert natural text from medical notes to vector representations suitable for deep learning algorithms, (2) how to help healthcare researchers select a patient …
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Using Concept Maps as a Tool for Cross-Language Relevance Determination
Concept maps, introduced by Novak, aid learners' understanding. I hypothesize that concept maps also can function as a summary of large documents, e.g., electronic theses and dissertations (ETDs). I have built a system that automatically generates concept maps from English-language ETDs in the …
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Deep Learning Based Methods for Automatic Extraction of Syntactic Patterns and their Application for Knowledge Discovery
… the basis for more advanced language tasks like entity recognition, sentiment analysis, machine translation, and question answering. They allow to infer causality, identify hierarchies, and connect ideas within a text, ultimately enhancing the depth and accuracy of automated language processing. …
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Uncertainty Estimation on Natural Language Processing
… us to undertake tasks like text classification, entity recognition, and even crafting responses within a dialogue context. However, despite the expansive utility of NLP, it frequently necessitates a critical decision: whether to place trust in a model's predictions. To illustrate, consider a …
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Domain-agnostic named entity recognition on unstructured text
Named Entity Recognition (NER) is the task of extracting informing entities belonging to predefined semantic classes from raw text. These semantic classes could be general-purpose like a person, location or domain-specific like genes, protein names in biomedical texts. NER has widespread …