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

We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages Compared to existing widely used toolkits, Stanza features a language-agnostic fully neural pipeline for text analysis, including tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological feature tagging, dependency parsing, and named entity recognition We have trained Stanza on a total of 112 datasets, including the Universal Dependencies treebanks and other multilingual corpora, and show that the same neural architecture generalizes well and achieves competitive performance on all languages tested Additionally, Stanza includes a native Python interface to the widely used Java Stanford CoreNLP software, which further extends its functionality to cover other tasks such as coreference resolution and relation extraction Source code, documentation, and pretrained models for 66 languages are available at https://stanfordnlpgithubio/stanza/

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Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Longman Student Grammar of Spoken and Written English March 13th, 2019 These tell us what choices are available in the grammar but we also need to understand how these choices are used to create discourse in different situations The year 1999 saw the publication of a large scale grammar of English with the aim of meeting the above needs the Longman ielts house net, longman student grammar of spoken and written english, longman grammar of spoken and written english roffel, longman student grammar of spoken and written english pdf, longman grammar of spoken and written english libros, longmans student grammar of spoken and written english, english longman grammar of spoken and written eng free, longman student grammar of spoken and written english, longman grammar of spoken and written english pdf web, lms2 vu edu pk, longman student grammar of spoken and written english, longman grammar of spoken and written english wikipedia, longman student grammar of spoken and written english, download pdf longman grammar of spoken and written, longman student grammar of spoken and written english, longman grammar of spoken and written english amazon co, longman student grammar of spoken and written english, longman grammar of spoken and written english edoc pub, the languagelab library longman student grammar of, longman grammar of spoken and written english scribd, longman grammar of spoken and written english free, the longman grammar of spoken and written english, longman grammar of spoken and written english epdf tips, grammars of spoken english new outcomes of corpus, longman grammar of spoken and written english tesl ej, book reviews longman grammar of spoken and written english, longman student grammar of spoken and written english, longman grammar of spoken and written english worldcat org, douglas biber et al longman grammar of spoken and, project muse longman grammar of spoken and written, longman grammar of spoken and written english oxford, 9780582237261 longman student grammar of spoken and, longman student grammar of spoken and written english, pdf longman grammar of spoken and written english, longman student grammar of spoken and written english, longman grammar of spoken and written english google books, student grammar of spoken and written english workbook, longman grammar of spoken and written english goodreads, longman student grammar of spoken and written english, longman student grammar of spoken and written english le, longman student grammar of spoken and written english, longman grammar of spoken and written english co construction, longman student grammar of spoken and written english, longman student grammar of spoken and written english by, longman student grammar of spoken and written english workbook, longman grammar of spoken and written english douglas

「会話の文法」に関する一考察 : Longman Grammar of Spoken and Written Englishの場合

Languages, like genes, provide vital clues about human history1,2. The origin of the Indo-European language family is “the most intensively studied, yet still most recalcitrant, problem of historical linguistics”3. Numerous genetic studies of Indo-European origins have also produced inconclusive results4,5,6. Here we analyse linguistic data using computational methods derived from evolutionary biology. We test two theories of Indo-European origin: the ‘Kurgan expansion’ and the ‘Anatolian farming’ hypotheses. The Kurgan theory centres on possible archaeological evidence for an expansion into Europe and the Near East by Kurgan horsemen beginning in the sixth millennium BP7,8. In contrast, the Anatolian theory claims that Indo-European languages expanded with the spread of agriculture from Anatolia around 8,000–9,500 years bp9. In striking agreement with the Anatolian hypothesis, our analysis of a matrix of 87 languages with 2,449 lexical items produced an estimated age range for the initial Indo-European divergence of between 7,800 and 9,800 years bp. These results were robust to changes in coding procedures, calibration points, rooting of the trees and priors in the bayesian analysis.

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Language-tree divergence times support the Anatolian theory of Indo-European origin

The CoNLL-2012 shared task involved predicting coreference in three languages -- English, Chinese and Arabic -- using OntoNotes data. It was a follow-on to the English-only task organized in 2011. Until the creation of the OntoNotes corpus, resources in this subfield of language processing have tended to be limited to noun phrase coreference, often on a restricted set of entities, such as ACE entities. OntoNotes provides a large-scale corpus of general anaphoric coreference not restricted to noun phrases or to a specified set of entity types and covering multiple languages. OntoNotes also provides additional layers of integrated annotation, capturing additional shallow semantic structure. This paper briefly describes the OntoNotes annotation (coreference and other layers) and then describes the parameters of the shared task including the format, pre-processing information, evaluation criteria, and presents and discusses the results achieved by the participating systems. Being a task that has a complex evaluation history, and multiple evalation conditions, it has, in the past, been difficult to judge the improvement in new algorithms over previously reported results. Having a standard test set and evaluation parameters, all based on a resource that provides multiple integrated annotation layers (parses, semantic roles, word senses, named entities and coreference) that could support joint models, should help to energize ongoing research in the task of entity and event coreference.

CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes

The Penn Treebank, in its eight years of operation (1989–1996), produced approximately 7 million words of part-of-speech tagged text, 3 million words of skeletally parsed text, over 2 million words of text parsed for predicateargument structure, and 1.6 million words of transcribed spoken text annotated for speech disfluencies. This paper describes the design of the three annotation schemes used by the Treebank: POS tagging, syntactic bracketing, and disfluency annotation and the methodology employed in production. All available Penn Treebank materials are distributed by the Linguistic Data Consortium http://www.ldc.upenn.edu.

The Penn Treebank: An Overview

This paper reports the results of an attempt to recover the first-order subgrouping of the Indo-European family using a new computational method devised by the authors and based on a ‘perfect phylogeny’ algorithm. The methodology is also briefly described, and points of theory and methodology are addressed in connection with the experiment whose results are here reported.

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Indo‐European and Computational Cladistics

The York–Toronto–Helsinki Parsed Corpus of Old English Prose (YCOE) is a 15 million-word syntactically annotated corpus of Old English prose texts It was produced at the University of York, UK, between 2000 and 2003, by Ann Taylor, Anthony Warner, Susan Pintzuk and Frank Beths, with a grant from the English Arts and Humanities Research Board (B/RG/AN5907/APN9528) The YCOE is part of the English Parsed Corpora Series It was the third historical corpus to be completed in this format, and uses the same kind of annotation scheme as its sister corpora, the Penn–Helsinki Parsed Corpus of Middle English II (PPCME2), the York–Helsinki Parsed Corpus of Old English Poetry and the Penn–Helsinki Parsed Corpus of Early Modern English Two other corpora in the series, the parsed version of the Corpus of Early English Correspondence and the Penn Parsed Corpus of Modern British English, are currently under construction (at the University of York, UK, in cooperation with the University of Helsinki, Finland, and the University of Pennsylvania, USA, respectively)

The York-Toronto-Helsinki Parsed Corpus of Old English prose (YCOE)

This paper reports the discovery of a syntactic dialect difference between northern and southern Middle English in the grammatical implementation of the so-called "verb-second" constraint and argues that this difference is most likely a linguistic contact effect of the Viking invasions of northern and eastern England in the eighth and ninth centuries. In the South, the Middle English V2 constraint behaves as it had in Old English; that is, as a variant of the IP-V2 type, the type found in modern Yiddish and Icelandic. In the North, however, the constraint is of the CP-V2 type, as found in modern Mainland Scandinavian and in German or Dutch. It is argued that the northern form of V2 arises as a consequence of the collapse of agreement inflection in northern Middle English, which leads to a loss of V-to-I movement, just as happened in mainland Scandinavian when agreement inflection was lost there. Once, V-to-I movement is lost, IP can no longer host the topic-verb configuration that defines the verb-second phenomenon. The collapse of agreement is further argued to result from imperfect second language learning by the Scandinavian invaders, who formed a large enough proportion of the population of the North to impose substratum effects on the northern dialect. Investigation of the available documentary evidence is shown to support dating both the collapse of endings and the appearance of CP-V2 word order to the period of the Viking invasions. It is suggested that the loss of V2 word order in English, which has made it the only Germanic language that does not respect the constraint, may have resulted from the extensive contact between northern and southern speakers that lead to the mixture of northern and southern features that characterize the language of the Midlands (and London) in the late Middle English period.

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Ann Taylor

Papers

The Penn Treebank: An Overview

Indo‐European and Computational Cladistics

The York-Toronto-Helsinki Parsed Corpus of Old English prose (YCOE)

The Middle English Verb-Second Constraint: A case study in language contact and languagechange

The Loss of OV Order in the History of English