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 54 for “"relation extraction"”.
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Evidence-enhanced document-level relation extraction
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
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Exploratory relation extraction in large multilingual data
The task of Relation Extraction (RE) is concerned with creating extractors that automatically find structured, relational information in unstructured data such as natural language text. Motivated by an explosion of sources of readily available text data such as the Web, RE offers intriguing …
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Unsupervised Relation Extraction for E-Learning Applications
… (NLP) based approach that relies on semantic relations extracted using Information Extraction to automatically generate MCTs. Information Extraction (IE) is an NLP field used to recognise the most important entities present in a text, and the relations between those concepts, regardless of …
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Background knowledge in learning-based relation extraction
Made available in DSpace on 2012-09-18T21:14:11Z (GMT). No. of bitstreams: 2 Do_Quang.pdf: 614220 bytes, checksum: 85ceb0b26f41d6d99d587b3e4187f19f (MD5) license.txt: 4058 bytes, checksum: 6a9a79b721b490aae6cb1e54370d178a (MD5)
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Applying semantic relation extraction to information retrieval
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
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Topic-oriented open relation extraction with seed generation
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01
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Tailoring large language models for zero-shot relation extraction
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Indirect supervision for relation extraction using question-answer pairs
"Automatic relation extraction (RE) for types of interest is of great importance for interpreting massive text corpora in an efficient manner. For example, we want to identify the relationship ""president_of"" between entities ""Donald Trump"" and ""United States"" in a sentence expressing such a …
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A Flexible Framework for Relation Extraction in Multiple Domains
Relation Extraction (RE) refers to the problem of extracting semantic relationships between concepts in a given sentence, and is an important component of Natural Language Understanding (NLU). It has been popularly studied in both the general purpose as well as the medical domains, and researchers …
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End-to-End Relation Extraction via Syntactic Structures and Semantic Resources
Information Extraction (IE) aims at mapping texts into fixed structure representing the key information. A typical IE system will try to answer the questions like who are present in the text, what events happen and when these events happen. The task is making possible significant advances in …
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DOCUMENT-LEVEL RELATION EXTRACTION AND TEMPORAL REASONING WITH LARGE LANGUAGE MODELS
… of LLMs in two aspects: (1) Document-level Relation Extraction (DocRE), and (2) Temporal Reasoning.
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N-ary Cross-sentence Relation Extraction: From Supervised to Unsupervised Learning
Relation extraction is the problem of extracting relations between entities described in the text. Relations identify a common "fact" described by distinct entities. Conventional relation extraction approaches focus on supervised binary intra-sentence relations, where the assumption is relations …
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Relation extraction: exploring syntax parsing and constructing it as attention-like structure
Relation extraction has attracted scientists’ attention since early 21st centuries and it has been one of the common natural language processing (NLP) tasks. It is so important since it could extract semantic relationships from corpus. There are several subtasks in relation extraction area, …
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Groundtruth budgeting : a novel approach to semi-supervised relation extraction in medical language
We address the problem of weakly-supervised relation extraction in hospital discharge summaries. Sentences with pre-identified concept types (for example: medication, test, problem, symptom) are labeled with the relationship between the concepts. We present a novel technique for weakly-supervised …
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A medication extraction framework for electronic health records
This thesis addresses the problem of concept and relation extraction in medical documents. We present a medical concept and relation extraction system (medNERR) that incorporates hand-built rules and constrained conditional models. We focus on two concept types (i.e., medications and medical …
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Event time representation, propagation and prediction in temporal information extraction
Temporal information extraction is a challenging task due to the inherent ambiguity of language. Event time plays an important role in temporal information extraction, which can help ground events into a timeline and can help other temporal information extraction tasks such as temporal relation …
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Temporal Reasoning in Clinical Narratives: From Information Extraction to Early Disease Detection
… a deeper exploration into concept and temporal relation extraction from clinical narratives. Towards this, I introduce GraphTREx, a state-of-the-art temporal relation extraction approach that achieves a 5% F1 improvement on the end-to-end temporal relation extraction task in the I2B2 2012 …
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Information extraction with neural networks
… Finally, 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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Domain knowledge acquisition via language grounding
… language grounding problem at the level of word relation extraction. We propose methods to acquire knowledge represented in the form of relations and utilize them in two domain applications, high-level planning in a complex virtual world and input parser generation from input format …
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