nlp thesis pdf

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

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

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

Recently, NLP is witnessing rapid progresses driven by Transformer models with the attention mechanism. Though enjoying the high performance, Transformers are challenging to deploy due to the intensive computation. In this thesis, we present an algorithm-hardware co-design approach to enable efficient Transformer inference.

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

use a function defined as. 1. sigmoid(x)c =. 1 + e x. (3.2)xto compute the probability for the output vector (i.e. outcome vector or valuesc 2 C. Cof output units) to belong to class , with denoting the set of a. of our problem. During tr. ining the model's output is compared to th. loss function.

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

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

Stanford NLP group, which was so much fun to work with, including officemates Roger Levy and Kristina Toutanova,and honorary officemates Teg Grenager and Ben Taskar. Finally, my deepest thanks for the love and support of my family. To my grandfathers Joseph Klein and Herbert Miller: I love and miss you both. To mymomJanandtoJenn,

1.1 Thesis Proposal The thesis will consist of two parts. In the first part, described in Section2, we will consider var-ious theories and properties of meaning from the point of view of philosophy of language. We will find which aspects of these theories are useful to describe the process of representing meaning in neural networks in NLP.

Institut fur Maschinelle Sprachverarbeitung Universit at Stuttgart Pfa enwaldring 5B D-70569 Stuttgart Machine Translation with Transformers Truong Thinh Nguyen

a long time. While Transformers are "newcomers" in NLP, they were developed on top of several ideas, the origins of which can be traced back to earlier work, such as word embedding (Bengio et al., 2003; Mikolov et al., 2013) and attention mechanisms (Bahdanau et al., 2014; Luong et al., 2015).

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

The paper distinguishes four phases by discussing. different levels of NLP and components of N atural L anguage G eneration (NLG) fo llowed by. presenting the history and evolution of NLP, state ...

improvement could be achieved by incorporating classical NLP engineering tricks into our systems. We then conclude with a short discussion section. 2. The Benchmark Tasks In this section, we briefly introduce four classical NLP tasks on which we will benchmark our architectures within this paper: Part-Of-Speech tagging (POS), chunking (CHUNK),

Abstract. This thesis focused on two tasks of applying natural language processing (NLP) and machine learning to electronic health records (EHRs) to improve clinical decision making. The first task was to predict cardiac resynchronization therapy (CRT) outcomes with better precision than the current physician guidelines for recommending the ...

built much of the interface in chapter 4. Together we also organized a workshop on NLP and HCI at ACL 2014. Sida, whose mathematical maturity and empirical intuition I instantly came to rely on, worked out the connections between various online learning algorithms in section 5.1. I spent many productive—and very late—nights working with ...

Abstract. Information sharing is one of the established approaches to improve demand forecasting and reduce the bullwhip effect, but it is infeasible to do so effectively in a long supply chain. Using the polystyrene industry as a case study, this thesis explores the usage of modern natural language processing (NLP) techniques in a deep ...

Natural Language Processing (NLP) is a way of analyzing texts by computerized means. NLP involves gathering of knowledge on how human beings understand and use language. This is done in order to ...

Abstract and Figures. Natural language processing (NLP) deals with building computational techniques that allow computers to automatically analyze and meaningfully represent human language. With ...

Shaodan MaAbstract—Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligenc. (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key rol.

In order to capture these complex associations expressed in a wide variety of textual data, including social media posts, interviews, and clinical notes, natural language processing (NLP) methods ...

−1. Joan Bresnan. 1972.Theory of complementation in English syntax. Ph.D. thesis, Massachusetts Institute of Technology, Department of Foreign Literatures and Linguistics. 321 pp.