James Pustejovsky

Amber Stubbs

Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started. Using detailed examples at every step, you’ll learn how the MATTER Annotation Development Process helps you Model, Annotate, Train, Test, Evaluate, and Revise your training corpus. You also get a complete walkthrough of a real-world annotation project. Define a clear annotation goal before collecting your dataset (corpus) Learn tools for analyzing the linguistic content of your corpus Build a model and specification for your annotation project Examine the different annotation formats, from basic XML to the Linguistic Annotation Framework Create a gold standard corpus that can be used to train and test ML algorithms Select the ML algorithms that will process your annotated data Evaluate the test results and revise your annotation task Learn how to use lightweight software for annotating texts and adjudicating the annotations This book is a perfect companion to O’Reilly’s Natural Language Processing with Python.

Natural Language Annotation for Machine Learning

Inderjeet Mani

Oxford linguistics series statement from dust jacket.

Grounded Representations for Spatial Language

Interpreting Motion

Pierrette Bouillon

Chungmin Lee

Hitoshi Isahara

Kyoko Kanzaki

This collection of papers takes linguists to the leading edge of techniques in generative lexicon theory, the linguistic composition methodology that arose from the imperative to provide a compositional semantics for the contextual modifications in meaning that emerge in real linguistic usage. Today’s growing shift towards distributed compositional analyses evinces the applicability of GL theory, and the contributions to this volume, presented at three international workshops (GL-2003, GL-2005 and GL-2007) address the relationship between compositionality in language and the mechanisms of selection in grammar that are necessary to maintain this property. The core unresolved issues in compositionality, relating to the interpretation of context and the mechanisms of selection, are treated from varying perspectives within GL theory, including its basic theoretical mechanisms and its analytical viewpoint on linguistic phenomena.

Advances in Generative Lexicon Theory

Nancy Ide

This handbook offers a thorough treatment of the science of linguistic annotation. Leaders in the field guide the reader through the process of modeling, creating an annotation language, building a corpus and evaluating it for correctness. Essential reading for both computer scientists and linguistic researchers.Linguistic annotation is an increasingly important activity in the field of computational linguistics because of its critical role in the development of language models for natural language processing applications. Part one of this book covers all phases of the linguistic annotation process, from annotation scheme design and choice of representation format through both the manual and automatic annotation process, evaluation, and iterative improvement of annotation accuracy. The second part of the book includes case studies of annotation projects across the spectrum of linguistic annotation types, including morpho-syntactic tagging, syntactic analyses, a range of semantic analyses (semantic roles, named entities, sentiment and opinion), time and event and spatial analyses, and discourse level analyses including discourse structure, co-reference, etc. Each case study addresses the various phases and processes discussed in the chapters of part one.

Handbook of Linguistic Annotation

A Guide to Corpus-Building for Applications

Olga Batiukova

An accessible introduction to lexical structure and design, and the relation of the lexicon to grammar as a whole. The Lexicon can be used for introductory and advanced courses, and includes a range of exercises and in-class activities designed to engage students, and help them acquire the knowledge and skills they need.

The Lexicon

In contrast to the static view of word meaning (where each word is characterized by a predetermined number of word senses) that imposes a tremendous bottleneck on the performance capability of any natural language processing system, Pustejovsky proposes that the lexicon becomes an active - and centralcomponent in the linguistic description. The essence of his theory is that the lexicon functions generatively, first by providing a rich and expressive vocabulary for characterizing lexical information; then, by developing a framework for manipulating fine-grained distinctions in word descriptions; and finally, by formalizing a set of mechanisms for specialized composition of aspects of such descriptions of words, as they occur in context, extended and novel senses are generated.

James Pustejovsky

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