Semantic Role Labeling
Semantic Role Labeling

Author: Martha Palmer

Publisher: Morgan & Claypool Publishers

Published: 2011-02-02

Total Pages: 103

ISBN-13: 1598298321

DOWNLOAD EBOOK

This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented. Table of Contents: Preface / Semantic Roles / Available Lexical Resources / Machine Learning for Semantic Role Labeling / A Cross-Lingual Perspective / Summary

Hands-On Natural Language Processing with Python
Hands-On Natural Language Processing with Python

Author: Rajesh Arumugam

Publisher: Packt Publishing Ltd

Published: 2018-07-18

Total Pages: 307

ISBN-13: 1789135915

DOWNLOAD EBOOK

Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features Weave neural networks into linguistic applications across various platforms Perform NLP tasks and train its models using NLTK and TensorFlow Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. What you will learn Implement semantic embedding of words to classify and find entities Convert words to vectors by training in order to perform arithmetic operations Train a deep learning model to detect classification of tweets and news Implement a question-answer model with search and RNN models Train models for various text classification datasets using CNN Implement WaveNet a deep generative model for producing a natural-sounding voice Convert voice-to-text and text-to-voice Train a model to convert speech-to-text using DeepSpeech Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.

The Oxford Handbook of Computational Linguistics
The Oxford Handbook of Computational Linguistics

Author: Ruslan Mitkov

Publisher: Oxford University Press

Published: 2004

Total Pages: 808

ISBN-13: 019927634X

DOWNLOAD EBOOK

This handbook of computational linguistics, written for academics, graduate students and researchers, provides a state-of-the-art reference to one of the most active and productive fields in linguistics.

Semantic Role Labeling
Semantic Role Labeling

Author: Martha Palmer

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 95

ISBN-13: 3031021355

DOWNLOAD EBOOK

This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented. Table of Contents: Preface / Semantic Roles / Available Lexical Resources / Machine Learning for Semantic Role Labeling / A Cross-Lingual Perspective / Summary

Robust Semantic Role Labeling
Robust Semantic Role Labeling

Author: Yi Szu-Ting

Publisher: LAP Lambert Academic Publishing

Published: 2015-05-25

Total Pages: 172

ISBN-13: 9783659691966

DOWNLOAD EBOOK

Correctly identifying semantic entities and successfully disambiguating the relations between them and their predicates is an important and necessary step for successful natural language processing applications, such as text summarization, question answering, and machine translation. Researchers have studied this problem, semantic role labeling (SRL), as a machine learning problem since 2000. However, after using an optimal global inference algorithm to combine several SRL systems, the growth of SRL performance seems to have reached a plateau. Syntactic parsing is the bottleneck of the task of semantic role labeling and robustness is the ultimate goal. In this book, we investigate ways to train a better syntactic parser and increase SRL system robustness. We demonstrate that parse trees augmented by semantic role markups can serve as suitable training data for training a parser for an SRL system. For system robustness, we propose that it is easier to learn a new set of semantic roles. The new roles are less verb- dependent than the original PropBank roles. As a result, the SRL system trained on the new roles achieves significantly better robustness.

Memory-Based Language Processing
Memory-Based Language Processing

Author: Walter Daelemans

Publisher: Cambridge University Press

Published: 2005-09-01

Total Pages: 199

ISBN-13: 1139445367

DOWNLOAD EBOOK

Memory-based language processing - a machine learning and problem solving method for language technology - is based on the idea that the direct reuse of examples using analogical reasoning is more suited for solving language processing problems than the application of rules extracted from those examples. This book discusses the theory and practice of memory-based language processing, showing its comparative strengths over alternative methods of language modelling. Language is complex, with few generalizations, many sub-regularities and exceptions, and the advantage of memory-based language processing is that it does not abstract away from this valuable low-frequency information. By applying the model to a range of benchmark problems, the authors show that for linguistic areas ranging from phonology to semantics, it produces excellent results. They also describe TiMBL, a software package for memory-based language processing. The first comprehensive overview of the approach, this book will be invaluable for computational linguists, psycholinguists and language engineers.

Intelligent Systems and Applications
Intelligent Systems and Applications

Author: Kohei Arai

Publisher: Springer

Published: 2018-11-07

Total Pages: 1292

ISBN-13: 3030010570

DOWNLOAD EBOOK

Gathering the Proceedings of the 2018 Intelligent Systems Conference (IntelliSys 2018), this book offers a remarkable collection of chapters covering a wide range of topics in intelligent systems and computing, and their real-world applications. The Conference attracted a total of 568 submissions from pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer review process, after which 194 (including 13 poster papers) were selected to be included in these proceedings. As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have made it possible to tackle many problems more effectively. This branching out of computational intelligence in several directions, and the use of intelligent systems in everyday applications, have created the need for such an international conference, which serves as a venue for reporting on cutting-edge innovations and developments. This book collects both theory and application-based chapters on all aspects of artificial intelligence, from classical to intelligent scope. Readers are sure to find the book both interesting and valuable, as it presents state-of-the-art intelligent methods and techniques for solving real-world problems, along with a vision of future research directions.