Latest 12+ Interesting Natural Language Processing Thesis Topics
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(PDF) Natural Language Processing approach to NLP Meta model automation
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NLP in the Real World
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Галичанский В., Янаков Э., Гаибов Д.
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Introduction: How to Interpret AI, ML, NLP, and Lisp (Lecture 1; August 23, 2017)
AI Innovations and Trends 05: iText2KG, Meta-Chunking, and gptpdf
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PDF Linguistic Knowledge in Data-Driven Natural Language Processing
tion of learned distributed representations. The scientific contributions of this thesis include a range of answers to new research questions and new statistical models; the practical contribu-tions are new tools and data resources, and several quantitatively and qualitatively improved NLP applications. ii
PDF RECURSIVE DEEP LEARNING A DISSERTATION
The models in this thesis address these two shortcomings. They provide e ective and general representations for sentences without assuming word order independence. Furthermore, they provide state of the art performance with no, or few manually designed features. The new model family introduced in this thesis is summarized under the term
PDF Analysis of Natural Language Processing (NLP) approaches to determine
Processing (NLP) approaches to determine semantic similarity between texts in domain-specific context Author: Surabhi Som (6248160) [email protected] Supervisors: Denis Paperno [email protected] Rick Nouwen [email protected] A thesis submitted in partial fulfillment of the requirements for the degree of Master of
PDF Building Robust Natural Language Processing Systems a Dissertionat
detection dataset, our method improves average precision from 2% to 32%. Overall, this thesis shows that state-of-the-art deep learning models have serious robustness defects, but also argues that by modifying di erent parts of the standard deep learning paradigm, we can make signi cant progress towards building robust NLP systems. v
PDF Thesis Proposal: People-Centric Natural Language Processing
In this thesis, I advocate for a model of text analysis that focuses on people, leveraging ideas from machine learning, the humanities and the social sciences. People intersect with text in multiple ways: they are its authors, its audience, and often the subjects of its content. While much current work in NLP
PDF Neural language models and human linguistic knowledge
In this thesis, I demonstrate this approach through three case studies using LMs to investigate open questions in language acquisition and comprehension. First, I use LMs to perform controlled manipulations of language learning, and find that syntactic gener-alizations depend more on a learner's inductive bias than on training data size. Second,
PDF MODELING NATURAL LANGUAGE SEMANTICS ADISSERTATION
chance on me as a novice NLP researcher my first year, I'm grateful to Georg Heigold for taking a chance on me as a novice neural networks researcher my second year, I'm grateful to Bill MacCartney for being a supportive mentor during my foray into semantic parsing my third year and for doing the work on applied natural logic that
PDF Natural Language Processing Methods for Dávid Márk Nemeskey Ph.D
and unsupervised representations supplanted linguistic features in NLP systems. Today, language modeling has become pervasive in all fields of NLP. In this thesis, we study the interaction between language modeling and NLP, with special focus on two aspects. First, most of the work done for language modeling, surely all
PDF Design Knowledge Base Using Natural Language Processing
Design Knowledge Base Using Natural Language Processing by Jack George Alexander Gammack BachelorofScienceinMechanicalEngineering, TheUniversityofTexasatAustin(2020)
PDF PredictingPriceResidualsinOnlineCar MarketplaceswithNaturalLanguage
In this context, the thesis covers the theory about recent state-of-the-art techniques in NLP and machine learning (ML). I focus on text classi˙cation here. Di˛erent types of word embeddings and Neural Networks with attention mechanisms are presented. I demonstrate that the vehicle description texts are on average very short and approximately ...
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tion of learned distributed representations. The scientific contributions of this thesis include a range of answers to new research questions and new statistical models; the practical contribu-tions are new tools and data resources, and several quantitatively and qualitatively improved NLP applications. ii
The models in this thesis address these two shortcomings. They provide e ective and general representations for sentences without assuming word order independence. Furthermore, they provide state of the art performance with no, or few manually designed features. The new model family introduced in this thesis is summarized under the term
Processing (NLP) approaches to determine semantic similarity between texts in domain-specific context Author: Surabhi Som (6248160) [email protected] Supervisors: Denis Paperno [email protected] Rick Nouwen [email protected] A thesis submitted in partial fulfillment of the requirements for the degree of Master of
detection dataset, our method improves average precision from 2% to 32%. Overall, this thesis shows that state-of-the-art deep learning models have serious robustness defects, but also argues that by modifying di erent parts of the standard deep learning paradigm, we can make signi cant progress towards building robust NLP systems. v
In this thesis, I advocate for a model of text analysis that focuses on people, leveraging ideas from machine learning, the humanities and the social sciences. People intersect with text in multiple ways: they are its authors, its audience, and often the subjects of its content. While much current work in NLP
In this thesis, I demonstrate this approach through three case studies using LMs to investigate open questions in language acquisition and comprehension. First, I use LMs to perform controlled manipulations of language learning, and find that syntactic gener-alizations depend more on a learner's inductive bias than on training data size. Second,
chance on me as a novice NLP researcher my first year, I'm grateful to Georg Heigold for taking a chance on me as a novice neural networks researcher my second year, I'm grateful to Bill MacCartney for being a supportive mentor during my foray into semantic parsing my third year and for doing the work on applied natural logic that
and unsupervised representations supplanted linguistic features in NLP systems. Today, language modeling has become pervasive in all fields of NLP. In this thesis, we study the interaction between language modeling and NLP, with special focus on two aspects. First, most of the work done for language modeling, surely all
Design Knowledge Base Using Natural Language Processing by Jack George Alexander Gammack BachelorofScienceinMechanicalEngineering, TheUniversityofTexasatAustin(2020)
In this context, the thesis covers the theory about recent state-of-the-art techniques in NLP and machine learning (ML). I focus on text classi˙cation here. Di˛erent types of word embeddings and Neural Networks with attention mechanisms are presented. I demonstrate that the vehicle description texts are on average very short and approximately ...