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Resourceful at Any Size: A Predictive Methodology Using Linguistic Corpus Metrics for Multi-Source Training in Neural Dependency Parsing
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| 2 |
ASR and Human Recognition Errors: Predictability and Lexical Factors
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| 4 |
Using External Knowledge to Improve Brown Clustering
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Abstract:
Thesis (Master's)--University of Washington, 2020 ; In recent years, semi-supervised learning methods that rely on using low-dimensional word representation gained interest in NLP, due to their ability take advantage of vastly available unlabeled data and reduce dependence on large, labeled datasets. In this thesis we propose two methods which allow integration of domain specific knowledge to one of the most popular methods for low dimensional word representations – Brown clustering. First, we propose changing the order in which words are clustered so that words more relevant for the task are given priority. Then, we also propose modifying the clustering objective so that it ensures relevance of induced clusters for downstream supervised task. Experiments show that both methods improve performance of NER system using cluster features.
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Keyword:
Artificial intelligence; brown clustering; Computer science; constrained clustering; Linguistics; ner; semi-supervised learning; word representations
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URL: http://hdl.handle.net/1773/46828
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| 5 |
The Language of Law: An Analysis of Gender and Turn-Taking in U.S. Supreme Court Oral Arguments
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Speech to Text to Semantics: A Sequence-to-Sequence System for Spoken Language Understanding
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| 8 |
Exploring Phone Recognition in Pre-verbal and Dysarthric Speech
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| 10 |
Labeling and Automatically Identifying Basic-Level Categories
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| 11 |
Three Cheers For Partisanship: Lexical Framing and Applause in U.S. Presidential Primary Debates
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Detection of Agreement and Disagreement: An investigation of linguistic coordination and conversational features
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