《Ｏｘｆｏｒｄ Ａｄｖａｎｃｅｄ Ｌｅａｒｎｅｒ’ｓ Ｄｉｃｔｉｏｎａｒｙ ｏｆ Ｃｕｒｒｅｎｔ Ｅｎｇｌｉｓｈ》（ 以下简称ＯＡＬＤ） 是英语学习词典的先驱。它是第一部既重视词语释义，又重视词法、句法，配有大量例证的词典。我国英语学习者对其第二、三、四版（ 英汉双解版） 都十分熟悉。１９９５ 年，ＯＡＬＤ 第五次修订再版，又有了新的特色，但仍有不足

评《Oxford Advanced Learner’s Dictionary of Current English》第五版

The Cambridge Grammar of the English Language

The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

Multiword expressions are a key problem for the development of large-scale, linguistically sound natural language processing technology. This paper surveys the problem and some currently available analytic techniques. The various kinds of multiword expressions should be analyzed in distinct ways, including listing "words with spaces", hierarchically organized lexicons, restricted combinatoric rules, lexical selection, "idiomatic constructions" and simple statistical affinity. An adequate comprehensive analysis of multiword expressions must employ both symbolic and statistical techniques.

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Multiword Expressions: A Pain in the Neck for NLP

Each year the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their systems on exactly the same data sets, in order to better compare systems. The tenth CoNLL (CoNLL-X) saw a shared task on Multilingual Dependency Parsing. In this paper, we describe how treebanks for 13 languages were converted into the same dependency format and how parsing performance was measured. We also give an overview of the parsing approaches that participants took and the results that they achieved. Finally, we try to draw general conclusions about multi-lingual parsing: What makes a particular language, treebank or annotation scheme easier or harder to parse and which phenomena are challenging for any dependency parser?

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CoNLL-X Shared Task on Multilingual Dependency Parsing

We present a treebank project for French. We have annotated a newspaper corpus of 1 Million words with part of speech, inflection, compounds, lemmas and constituency. We describe the tagging and parsing phases of the project, and for each, the automatic tools, the guidelines and the validation process. We then present some uses of the corpus as well as some directions for future work.

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Building a Treebank for French

This paper presents a sizable grammar for English written in the Tree Adjoining grammar (TAG) formalism. The grammar uses a TAG that is both lexicalized (Schabes, Abeille, Joshi 1988) and feature-based (VijayShankar, Joshi 1988). In this paper, we describe a wide range of phenomena that it covers. A Lexicalized TAG (LTAG) is organized around a lexicon, which associates sets of elementary trees (instead of just simple categories) with the lexical items. A Lexicalized TAG consists of a finite set of trees associated with lexical items, and operations (adjunction and substitution) for composing the trees. A lexical item is called the anchor of its corresponding tree and directly determines both the tree's structure and its syntactic features. In particular, the trees define the domain of locality over which constraints are specified and these constraints are local with respect to their anchor. In this paper, the basic tree structures of the English LTAG are described, along with some relevant features. The interaction between the morphological and the syntactic components of the lexicon is also explained. Next, the properties of the different tree structures are discussed. The use of S complements exclusively allows us to take full advantage of the treatment of unbounded dependencies originally presented in Joshi (1985) and Kroch and Joshi (1985). Structures for auxiliaries and raising-verbs which use adjunction trees are also discussed. We present a representation of prepositional complements that is based on extended elementary trees. This representation avoids the need for preposition incorporation in order to account for double whquestions (preposition stranding and pied-piping) and the pseudo-passive. A treatment of light verb constructions is also given, similar to what Abeille (1988c) has presented. Again, neither noun nor adjective incorporation is needed to handle double passives and to account for CNPC violations in these constructions. TAG'S extended domain of locality allows us to handle, within a single level of syntactic description, phenomena that in other frameworks require either dual analyses or reanalysis. In addition, following Abeille and Schabes (1989), we describe how to deal with semantic non compositionality in verb-particle combinations, light verb constructions and idioms, without losing the internal syntactic composition of these structures. The last sections discuss current work on PRO, case, anaphora and negation, and outline future work on copula constructions and small clauses, optional arguments, adverb movement and the nature of syntactic rules in a lexicalized framework. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-90-24. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/527 A Lexicalized Tree Adjoining Grammar For English MS-CIS-90-24 LINC LAB 170 Anne Abeillh Kathleen Bishop Sharon Cote Yves Schabes Department of Computer and Information Science School of Engineering and Applied Science University of Pennsylvania Philadelphia, PA 19104-6389

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A Lexicalized Tree Adjoining Grammar for English

In this paper we present a general parsing strategy that arose from the development of an Earley-type parsing algorithm for TAGs (Schabes and Joshi 1988) and from recent linguistic work in TAGs (Abeille 1988).In our approach elementary structures are associated with their lexical heads. These structures specify extended domains of locality (as compared to a context-free grammar) over which constraints can be stated. These constraints either hold within the elementary structure itself or specify what other structures can be composed with a given elementary structure.We state the conditions under which context-free based grammars can be 'lexicalized' without changing the linguistic structures originally produced. We argue that even if one extends the domain of locality of CFGs to trees, using only substitution does not give the freedom to choose the head of each structure. We show how adjunction allows us to 'lexicalize' a CFG freely.We then show how a 'lexicalized' grammar naturally follows from the extended domain of locality of TAGs and present some of the linguistic advantages of our approach.A novel general parsing strategy for 'lexicalized' grammars is discussed. In a first stage, the parser builds a set structures corresponding to the input sentence and in a second stage, the sentence is parsed with respect to this set. The strategy is independent of the linguistic theory adopted and of the underlying grammar formalism. However, we focus our attention on TAGs. Since the set of trees needed to parse an input sentence is supposed to be finite, the parser can use in principle any search strategy. Thus, in particular, a top-down strategy can be used since problems due to recursive structures are eliminated. The parser is also able to use non-local information to guide the search.We then explain how the Earley-type parser for TAGs can be modified to take advantage of this approach.

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Parsing strategies with 'lexicalized' grammars: application to tree adjoining grammars

Preface. Introduction A. Abeille. I: Building Treebanks. English Treebanks. 1. The Penn treebank: an overview A. Taylor, M. Marcus, B. Santorini. 2. Thoughts on two decades of drawing trees G. Sampson. 3. Bank of English and beyond T. Jarvinen. 4. Completing parsed corpora from correction to evolution S. Wallis. German Treebanks. 5. Syntactic annotation of a German newspaper corpus T. Brants, W. Skut, H. Uszkoreit. 6. Annotation of error types for a German newsgroup corpus M. Becker, A. Bredenkamp, B. Crysmann, J. Klein. Slavic Treebanks. 7. The PDT: a 3-level annotation scenario A. Boehmova, J. Hajic, E. Hajicova, B. Hladka. 8. An HPSG-annotated test suite for Polish M. Marciniak, A. Mykowiecka, A. Przepiorkowski, A. Kupsc. Treebanks for Romance Languages. 9. Developing a Spanish treebank A. Moreno, S. Lopez, F. Sanchez, R. Grishman. 10. Building a treebank for French A. Abeille, L. Clement, F. Toussenel. 11. Building the Italian syntactic-semantic treebank S. Montemagni, F. Barsotti, M. Battista, N. Calzolari, O. Corazzari, A. Lenci, A. Zampolli, F. Fanciulli, M. Massetani, R. Raffaelli, R. Basili, M.T. Pazienza, D. Saracino, F. Zanzotto, N. Mana, F. Pianesi, R. Delmonte. 12. Automated creation of a medieval Portugese treebank V. Rocio, M. Amado Alves, J.G. Lopes, M.F. Xavier, G. Vicente. Treebanks for other Languages. 13. Sinica treebank Keh-Jiann Chen, Chi-Ching Luo, Ming-Chung Chang, Feng-Yi Chen, Chao-Jan Chen, Chu-Ren Huang, Zhao-Ming Gao.14. Building a Japanese parsed corpus S. Kurohashi, M. Nagao. 15. Building a Turkish treebank K. Oflazer, B. Say, D. Zeynep Hakkani-Tur, G. Tur. II: Using Treebanks. 16. Encoding syntactic annotation N. Ide, L. Romary. Evaluation with Treebanks. 17. Parser evaluation J. Carroll, G. Minnen, T. Briscoe. 18. Dependency-based evaluation of minipar Dekang Lin. Grammar Induction with Treebanks. 19. Extracting stochastic grammars from treebanks R. Bod. 20. Stochastic lexicalized tree grammars G. Neumann. 21. From treebank resources to LFG f-structures A. Frank, L. Sadler, J. van Genabith, A. Way. Contributing Authors. Index.

Treebanks Building and Using Parsed Corpora

Lexicalized Tree Adjoining Grammar (LTAG) is an attractive formalism for linguistic description mainly because of its extended domain of locality and its factoring recursion out from the domain of local dependencies (Joshi, 1985, Kroch and Joshi, 1985, Abeille, 1988). LTAG's extended domain of locality enables one to localize syntactic dependencies (such as filler-gap), as well as semantic dependencies (such as predicate-arguments). The aim of this paper is to show that these properties combined with the lexicalized property of LTAG are especially attractive for machine translation.The transfer between two languages, such as French and English, can be done by putting directly into correspondence large elementary units without going through some interlingual representation and without major changes to the source and target grammars. The underlying formalism for the transfer is "synchronous Tree Adjoining Grammars" (Shieber and Schabes [1990]). Transfer rules are stated as correspondences between nodes of trees of large domain of locality which are associated with words. We can thus define lexical transfer rules that avoid the defects of a mere word-to-word approach but still benefit from the simplicity and elegance of a lexical approach.We rely on the French and English LTAG grammars (Abeille [1988], Abeille [1990 (b)], Abeille et al. [1990], Abeille and Schabes [1989, 1990]) that have been designed over the past two years jointly at University of Pennsylvania and University of Paris 7-Jussieu.

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Anne Abeillé

Papers

Building a Treebank for French

A Lexicalized Tree Adjoining Grammar for English

Parsing strategies with 'lexicalized' grammars: application to tree adjoining grammars

Treebanks Building and Using Parsed Corpora

Using lexicalized tags for machine translation