Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms. Key Features Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms Learn deep learning techniques for text analysis Book DescriptionModern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis. What you will learn Why text analysis is important in our modern age Understand NLP terminology and get to know the Python tools and datasets Learn how to pre-process and clean textual data Convert textual data into vector space representations Using spaCy to process text Train your own NLP models for computational linguistics Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn Employ deep learning techniques for text analysis using Keras Who this book is forThis book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!

Perform efficient fast text representation and classification with Facebook's fastText library Key Features Introduction to Facebook's fastText library for NLP Perform efficient word representations, sentence classification, vector representation Build better, more scalable solutions for text representation and classification Book DescriptionFacebook's fastText library handles text representation and classification, used for Natural Language Processing (NLP). Most organizations have to deal with enormous amounts of text data on a daily basis, and gaining efficient data insights requires powerful NLP tools such as fastText. This book is your ideal introduction to fastText. You will learn how to create fastText models from the command line, without the need for complicated code. You will explore the algorithms that fastText is built on and how to use them for word representation and text classification. Next, you will use fastText in conjunction with other popular libraries and frameworks such as Keras, TensorFlow, and PyTorch. Finally, you will deploy fastText models to mobile devices. By the end of this book, you will have all the required knowledge to use fastText in your own applications at work or in projects. What you will learn Create models using the default command line options in fastText Understand the algorithms used in fastText to create word vectors Combine command line text transformation capabilities and the fastText library to implement a training, validation, and prediction pipeline Explore word representation and sentence classification using fastText Use Gensim and spaCy to load the vectors, transform, lemmatize, and perform other NLP tasks efficiently Develop a fastText NLP classifier using popular frameworks, such as Keras, Tensorflow, and PyTorch Who this book is forThis book is for data analysts, data scientists, and machine learning developers who want to perform efficient word representation and sentence classification using Facebook's fastText library. Basic knowledge of Python programming is required.

With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book DescriptionRecommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory-you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is forIf you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.

Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator Key Features Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks Implement agents to solve simple to complex AI problems Study learning environments and discover how to create your own Book DescriptionMany real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level. What you will learn Explore intelligent agents and learning environments Understand the basics of RL and deep RL Get started with OpenAI Gym and PyTorch for deep reinforcement learning Discover deep Q learning agents to solve discrete optimal control tasks Create custom learning environments for real-world problems Apply a deep actor-critic agent to drive a car autonomously in CARLA Use the latest learning environments and algorithms to upgrade your intelligent agent development skills Who this book is forIf you're a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.

Build and deploy powerful neural network models using the latest Java deep learning libraries Key Features Understand DL with Java by implementing real-world projects Master implementations of various ANN models and build your own DL systems Develop applications using NLP, image classification, RL, and GPU processing Book DescriptionJava is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you'll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems. What you will learn Master deep learning and neural network architectures Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs Train ML agents to learn from data using deep reinforcement learning Use factorization machines for advanced movie recommendations Train DL models on distributed GPUs for faster deep learning with Spark and DL4J Ease your learning experience through 69 FAQs Who this book is forIf you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required.

Write modern natural language processing applications using deep learning algorithms and TensorFlow Key Features Focuses on more efficient natural language processing using TensorFlow Covers NLP as a field in its own right to improve understanding for choosing TensorFlow tools and other deep learning approaches Provides choices for how to process and evaluate large unstructured text datasets Learn to apply the TensorFlow toolbox to specific tasks in the most interesting field in artificial intelligence Book DescriptionNatural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today's data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks. What you will learn Core concepts of NLP and various approaches to natural language processing How to solve NLP tasks by applying TensorFlow functions to create neural networks Strategies to process large amounts of data into word representations that can be used by deep learning applications Techniques for performing sentence classification and language generation using CNNs and RNNs About employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasks How to write automatic translation programs and implement an actual neural machine translator from scratch The trends and innovations that are paving the future in NLP Who this book is forThis book is for Python developers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks. Fundamental Python skills are assumed, as well as some knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required, although some background in NLP or computational linguistics will be helpful.

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming. Key Features Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j Solve real-world machine learning problems by delving complex numerical computing with Scala functional programming in a scalable and faster way Cover all key aspects such as collection, storing, processing, analyzing, and evaluation required to build and deploy machine models on computing clusters using Scala Play framework. Book DescriptionMachine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment. What you will learn Apply advanced regression techniques to boost the performance of predictive models Use different classification algorithms for business analytics Generate trading strategies for Bitcoin and stock trading using ensemble techniques Train Deep Neural Networks (DNN) using H2O and Spark ML Utilize NLP to build scalable machine learning models Learn how to apply reinforcement learning algorithms such as Q-learning for developing ML application Learn how to use autoencoders to develop a fraud detection application Implement LSTM and CNN models using DeepLearning4j and MXNet Who this book is forIf you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Strong understanding of Scala Programming language is recommended. Basic familiarity with machine Learning techniques will be more helpful.

This book addresses the issue of how the user's level of domain knowledge affects interaction with a computer system. It demonstrates the feasibility of incorporating a model of user's domain knowledge into a natural language generation system.

Learn to build expert NLP and machine learning projects using NLTK and other Python libraries About This Book * Break text down into its component parts for spelling correction, feature extraction, and phrase transformation * Work through NLP concepts with simple and easy-to-follow programming recipes * Gain insights into the current and budding research topics of NLP Who This Book Is For If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master NLTK for natural language processing, then this Learning Path will do you a lot of good. Students of linguistics and semantic/sentiment analysis professionals will find it invaluable. What You Will Learn * The scope of natural language complexity and how they are processed by machines * Clean and wrangle text using tokenization and chunking to help you process data better * Tokenize text into sentences and sentences into words * Classify text and perform sentiment analysis * Implement string matching algorithms and normalization techniques * Understand and implement the concepts of information retrieval and text summarization * Find out how to implement various NLP tasks in Python In Detail Natural Language Processing is a field of computational linguistics and artificial intelligence that deals with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. The number of human-computer interaction instances are increasing so it's becoming imperative that computers comprehend all major natural languages. The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy. The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods. The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python. This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products: * NTLK essentials by Nitin Hardeniya * Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins * Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti Mathur Style and approach This comprehensive course creates a smooth learning path that teaches you how to get started with Natural Language Processing using Python and NLTK. You'll learn to create effective NLP and machine learning projects using Python and NLTK.

Spoken Dialogue Systems Technology and Design covers key topics in the field of spoken language dialogue interaction from a variety of leading researchers. It brings together several perspectives in the areas of corpus annotation and analysis, dialogue system construction, as well as theoretical perspectives on communicative intention, context-based generation, and modelling of discourse structure. These topics are all part of the general research and development within the area of discourse and dialogue with an emphasis on dialogue systems; corpora and corpus tools and semantic and pragmatic modelling of discourse and dialogue.

What are mental concepts? Why do they work the way they do? How can they be captured in language? How can they be captured in a computer? The authors describe the development of, and clearly explain, the underlying linguistic theory and the working software they have developed over 40 years to store declarative knowledge in a computer fully to the same level as language, knowledge accessible via ordinary conversation. During this 40 year project there was no epiphany, no "Eureka moment," except perhaps for the day that their parser program successfully parsed a long sentence for the first time, taking into account the contribution of every word and punctuation mark. Their parser software can now parse a whole paragraph of long sentences each comprising multiple subordinate clauses with punctuation, to determine the paragraph's global meaning. Among many practical applications for their technology is precision communication with the Internet. The authors show that knowledge stored in language is not unstructured as is generally assumed. Rather they show that language expressions are highly structured once the rules of syntax are understood. Lexical words, grammaticals, punctuation marks, paragraphs and poetry, single elimination tournaments, "grandmother cells," calculator algorithms are just a few of the topics explored in this smart, witty, and eclectic tour through natural language understanding by a computer. Illustrated with flow-of-meaning-trees and easily followed Mensa tables this essay outlines a wide-ranging theory of language and thought and its transition to computers. John W. Gorman, a Masters in Engineering from the University of Auckland, joined his father, John G. Gorman, Lasker Award winning medical researcher, in their enterprise twenty years ago to solve the until now intractable problem of computer understanding of thought and language. An Essay Concerning Computer Understanding will provoke linguists, neuroscientists, software designers, advertisers, poets, and the just plain curious. The book suggests many opportunities for future research in linguistic theory and cognitive science employing hands on experiments with computer models of knowledge and the brain. Discover the theory and practice of computer understanding that has computational linguists everywhere taking notice.

As its title suggests, this book is a selection of papers that use English corpora to study language variation along three dimensions - time, place and genre. In broad terms, the book aims to bridge the gap between corpus linguistics and sociolinguistics and to increase our knowledge of the characteristics of English language. It includes eleven papers which address a variety of research questions but with the commonality of a corpus-based methodology. Some of the contributions deal with language variation in time, either by looking into historical corpora of English or by adopting the method known as diachronic comparable corpus linguistics, thus illustrating how corpora can be used to illuminate either historical or recent developments of English. Other studies investigate variation in space by comparing different varieties of English, including some of the "New Englishes" such as the South Asian varieties of English. Finally, some of the papers deal with variation in genre, by looking into the use of language for specific purposes through the inspection of medical articles, social reports and academic writing.

ThoughtTreasure is a commonsense knowledge base and architecture for natural language processing. It uses multiple representations including logic, finite automata, grids, and scripts. The ThoughtTreasure architecture consists of: the text agency, containing text agents for recognizing words, phrases, and names, and mechanisms for learning new words and inflections; the syntactic component, containing a syntactic parser, base rules, and filters; the semantic component, containing a semantic parser for producing a surface-level understanding of a sentence, a natural language generator, and an anaphoric parser for resolving anaphoric entities such as pronouns; the planning agency, containing planning agents for achieving goals on behalf of simulated actors; and the understanding agency, containing understanding agents for producing a more detailed understanding of a discourse.

"Advances in Non-Linear Modeling for Speech Processing" includes advanced topics in non-linear estimation and modeling techniques along with their applications to speaker recognition.
Non-linear aeroacoustic modeling approach is used to estimate the important fine-structure speech events, which are not revealed by the short time Fourier transform (STFT). This aeroacostic modeling approach provides the impetus for the high resolution Teager energy operator (TEO). This operator is characterized by a time resolution that can track rapid signal energy changes within a glottal cycle.
The cepstral features like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the magnitude spectrum of the speech frame and the phase spectra is neglected. To overcome the problem of neglecting the phase spectra, the speech production system can be represented as an amplitude modulation-frequency modulation (AM-FM) model. To demodulate the speech signal, to estimation the amplitude envelope and instantaneous frequency components, the energy separation algorithm (ESA) and the Hilbert transform demodulation (HTD) algorithm are discussed.
Different features derived using above non-linear modeling techniques are used to develop a speaker identification system. Finally, it is shown that, the fusion of speech production and speech perception mechanisms can lead to a robust feature set.

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.

For 50 years the natural language interface has tempted and challenged researchers and the public in equal measure. As advanced domains such as robotic systems mature over the next ten years, the need for effective language interfaces will become more significant as the disparity between physical and language ability becomes more evident. Natural language conversation with robots and other situated systems will not only require a clear understanding of theories of language use, models of spatial representation and reasoning, and theories of intentional action and agency - but will also require that all of these models be made accessible within tractable dialogue processing frameworks. While such issues pose research questions which are significant, particularly when we consider them in the light of the many other challenges in language processing and spatial theory, the benefits of competence in situated dialogue to the fields of robotics, geographic information systems, game design, and applied artificial intelligence cannot be underestimated. This book examines the burgeoning field of Situated Dialogue Systems and describes for the first time a complete computational model of situated dialogue competence for practical dialogue systems. The book can be broadly broken down into two parts. The first three chapters examine on one hand the issues which complicate the computational modelling of situated dialogue, i.e., issues of agency and spatial language competence, and on the other hand examines theories of dialogue modelling and management with respect to the needs of the situated domain. The second part of the book then details a situated dialogue processing architecture. Novel features of this architecture include the modular integration of an intentionality model alongside an exchange-structure based organization of discourse, plus the use of a functional contextualization process that operates over both implicit and explicit content in user contributions. The architecture is described at a course level, but in sufficient detail for others to use as a starting point in their own explorations of situated language intelligence.

Signal Processing Methods for Music Transcription is the first book dedicated to uniting research related to signal processing algorithms and models for various aspects of music transcription such as pitch analysis, rhythm analysis, percussion transcription, source separation, instrument recognition, and music structure analysis. Following a clearly structured pattern, each chapter provides a comprehensive review of the existing methods for a certain subtopic while covering the most important state-of-the-art methods in detail. The concrete algorithms and formulas are clearly defined and can be easily implemented and tested. A number of approaches are covered, including, for example, statistical methods, perceptually-motivated methods, and unsupervised learning methods. The text is enhanced by a common reference and index.

In the 14 years since its ?rst edition back in 1997, the European Conference on Research and Advanced Technology for Digital Libraries (ECDL) has become the reference meeting for an interdisciplinary community of researchers and practitioners whose professional activities revolve around the theme of d- th ital libraries. This volume contains the proceedings of ECDL 2010, the 14 conference in this series, which, following Pisa (1997), Heraklion (1998), Paris (1999),Lisbon(2000),Darmstadt(2001),Rome(2002),Trondheim(2003),Bath (2004), Vienna (2005), Alicante (2006), Budapest (2007), Aarhus (2008), and Corfu (2009), was held in Glasgow, UK, during September 6-10, 2010. th Asidefrombeingthe14 edition of ECDL, this was also the last, at least with this name since starting with 2011, ECDL will be renamed (so as to avoid acronym con?icts with the European Computer Driving Licence) to TPLD, standing for the Conference on Theory and Practice of Digital Libraries. We hope you all will join us for TPDL 2011 in Berlin! For ECDL 2010 separate calls for papers, posters and demos were issued, - sulting in the submission to the conference of 102 full papers, 40 posters and 13 demos. This year, for the full papers, ECDL experimented with a novel, two-tier reviewing model, with the aim of further improving the quality of the resu- ing program. A ?rst-tier Program Committee of 87 members was formed, and a further Senior Program Committee composed of 15 senior members of the DL community was set up.

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