"Buenos dias", "buenas noches" -- this was the first words in a foreign language I heard in my life, as a three-year old boy growing up in developing post-war Western Germany, where the first gastarbeiters had arrived from Spain. Fascinated by the strange sounds, I tried to get to know some more languages, the only opportunity being TV courses of English and French -- there was no foreign language education for pre-teen school children in Germany yet in those days. Read more
To find some answers Tim Machan explores the language's present and past, and looks ahead to its futures among the one and a half billion people who speak it. His search is fascinating and important, for definitions of English have influenced education and law in many countries and helped shape the identities of those who live in them.
This volume provides a new perspective on the evolution of the special language of medicine, based on the electronic corpus of Early Modern English Medical Texts, containing over two million words of medical writing from 1500 to 1700.
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:
Available Lexical Resources
Machine Learning for Semantic Role Labeling
A Cross-Lingual Perspective