Ruslan Mitkov, Johanna Monti, Gloria Corpas Pastor, and Violeta Seretan (eds): Multiword units in machine translation and translation technology

The book Multiword Units in Machine Translation and Translation Technology demonstrates the importance of multiword units (MWUs) in natural language processing (NLP) applications, and explores computational treatments of how they can be handled in NLP, particularly in machine translation (MT) and translation technology. Thebookwas editedbyRuslanMitkov (University ofWolverhampton), JohannaMonti (L’Orientale University of Naples), Gloria Corpas Pastor (University of Málaga), and Violeta Seretan (University of Geneva) who are renowned researchers in NLP and computational linguistics. The book contains twelve chapters including an introductory chapter compiled by the editors of the book themselves. The remaining eleven chapters were contributed by NLP researchers and experts in the field, and each of these chapters investigates specific problems relating to MWUs. The introductory chapter (Chap. 1) has same title as the the book itself (Multiword Units in Machine Translation and Translation Technology), and presents a survey of the field with particular attention to MT and translation technology. At the start of the chapter, the authors present a comprehensive definition of MWUs with an example: “Multiword units or multiword expressions are meaningful lexical units made of two or more words in which at least one of them is restricted by linguistic conventions in the sense that it is not freely chosen. For example, in the expression to ‘smell a rat’ the

[1]  Carlos Ramisch,et al.  Multiword Expressions Acquisition , 2015, Theory and Applications of Natural Language Processing.

[2]  Carlos Ramisch,et al.  Multiword Expressions Acquisition: A Generic and Open Framework , 2014 .

[3]  Eric Wehrli,et al.  Collocations in a Rule-Based MT System: A Case Study Evaluation of their Translation Adequacy , 2009, EAMT.

[4]  Philipp Koehn,et al.  Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) , 2007 .

[5]  András Kornai,et al.  Parallel corpora for medium density languages , 2007 .

[6]  Stella Markantonatou,et al.  Parsing Modern Greek verb MWEs with LFG/XLE grammars , 2014, MWE@EACL.

[7]  Carlos Ramisch,et al.  Introduction to the special issue on multiword expressions: From theory to practice and use , 2013, TSLP.

[8]  Timothy Baldwin,et al.  Multiword Expressions: A Pain in the Neck for NLP , 2002, CICLing.

[9]  Philipp Koehn,et al.  Factored Translation Models , 2007, EMNLP.

[10]  Timothy Baldwin,et al.  Multiword Expressions , 2010, Handbook of Natural Language Processing.

[11]  Helmut Schmidt,et al.  Probabilistic part-of-speech tagging using decision trees , 1994 .

[12]  Michel Simard,et al.  Translation Spotting for Translation Memories , 2003, ParallelTexts@NAACL-HLT.

[13]  Philipp Koehn,et al.  Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[14]  Olatz Ansa,et al.  Representation and Treatment of Multiword Expressions in Basque , 2004 .

[15]  Carlos Ramisch,et al.  A Generic Framework for Multiword Expressions Treatment: from Acquisition to Applications , 2012, ACL 2012.

[16]  Ted Dunning,et al.  Accurate Methods for the Statistics of Surprise and Coincidence , 1993, CL.

[17]  J. Firth,et al.  Papers in linguistics, 1934-1951 , 1957 .

[18]  Aline Villavicencio,et al.  Automated Multiword Expression Prediction for Grammar Engineering , 2006 .

[19]  Ray Jackendoff,et al.  The Architecture of the Language Faculty , 1996 .