Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, (...) and other applications. (shrink)

We examine the occurrence of ethicsrelated terms in 10-K annual reports over 1994-2006 and offer empirical observations on the conceptual framework of Erhard et al. 2007). We use a pre-Sarbanes-Oxley sample subset to compare the occurrence of ethics-related terms in our 10-K data with samples from other studies that consider virtue-related phenomena. We find that firms using ethics-related terms are more likely to be "sin" stocks, are more likely to be the object of class action lawsuits, and are more likely (...) to score poorly on measures of corporate governance. The consistency of our results across these alternative measures of ethical behavior suggests that managers who portray their firm as "ethical" in 10-K reports are more likely to be systematically misleading the public. These results are consistent with the integrity-performance paradox. (shrink)

This paper reports on the findings of the third in a series of surveys of large U.S.-based and multinational corporations on their ethics statements. Focusing on four types – values statement, corporate credo, code of ethics and Internet privacy policy – we find growth in the use of these statements over the last decade. We discuss the external communication of these statements, including the avenues that are now used for promotion and their intended audiences. The paper concludes with a number (...) of research issues to be addressed. (shrink)