While supervised corpus-based methods are highly accurate for different NLP tasks, including morphological tagging, they are difficult to port to other languages because they require resources that are expensive to create. As a result, many languages have no realistic prospect for morpho-syntactic annotation in the foreseeable future. The method presented in this book aims to overcome this problem by significantly limiting the necessary data and instead extrapolating the relevant information from another, related language. The approach has been tested on Catalan, Portuguese, and Russian. Although these languages are only relatively resource-poor, the same method can be in principle applied to any inflected language, as long as there is an annotated corpus of a related language available. Time needed for adjusting the system to a new language constitutes a fraction of the time needed for systems with extensive, manually created resources: days instead of years.
This book touches upon a number of topics: typology, morphology, corpus linguistics, contrastive linguistics, linguistic annotation, computational linguistics and Natural Language Processing (NLP). Researchers and students who are interested in these scientific areas as well as in cross-lingual studies and applications will greatly benefit from this work. Scholars and practitioners in computer science and linguistics are the prospective readers of this book.
Anna Feldman is an assistant professor of linguistics and computer science at Montclair State University. She received her Ph.D. from The Ohio State University.
Jirka Hana is a researcher at Charles University in Prague. He holds a Ph.D. degree in linguistics from The Ohio State University and a doctoral degree in computer science from Charles University. He has published numerous articles in computational linguistics.
”F[eldman] & H[ana] have opened a very interesting door, showing us a method with many potential applications to less resourced languages. I suspect there are many other methods behind that door that we could put to use leveraging the computational analysis of one language to help analyze related languages. Finally, it is a potential way for field linguists and computational linguists to work together--again, after a lapse of some years.” in:
Linguist List, Fri. Dec. 17, 2010
Table of contents
List of tables
List of figures
Common tagging techniques
Previous resource-light approaches to NLP
Languages, corpora and tagsets
Quantifying language properties
Resource-light morphological analysis
Cross-language morphological tagging
Summary and further work
Appendices: Tagsets we use; Corpora; Language properties