Maurice Gross' Grammar Lexicon and Natural Language Processing
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Why Is Language Typology Possible?
Why is language typology possible? Martin Haspelmath 1 Languages are incomparable Each language has its own system. Each language has its own categories. Each language is a world of its own. 2 Or are all languages like Latin? nominative the book genitive of the book dative to the book accusative the book ablative from the book 3 Or are all languages like English? 4 How could languages be compared? If languages are so different: What could be possible tertia comparationis (= entities that are identical across comparanda and thus permit comparison)? 5 Three approaches • Indeed, language typology is impossible (non- aprioristic structuralism) • Typology is possible based on cross-linguistic categories (aprioristic generativism) • Typology is possible without cross-linguistic categories (non-aprioristic typology) 6 Non-aprioristic structuralism: Franz Boas (1858-1942) The categories chosen for description in the Handbook “depend entirely on the inner form of each language...” Boas, Franz. 1911. Introduction to The Handbook of American Indian Languages. 7 Non-aprioristic structuralism: Ferdinand de Saussure (1857-1913) “dans la langue il n’y a que des différences...” (In a language there are only differences) i.e. all categories are determined by the ways in which they differ from other categories, and each language has different ways of cutting up the sound space and the meaning space de Saussure, Ferdinand. 1915. Cours de linguistique générale. 8 Example: Datives across languages cf. Haspelmath, Martin. 2003. The geometry of grammatical meaning: semantic maps and cross-linguistic comparison 9 Example: Datives across languages 10 Example: Datives across languages 11 Non-aprioristic structuralism: Peter H. Matthews (University of Cambridge) Matthews 1997:199: "To ask whether a language 'has' some category is...to ask a fairly sophisticated question.. -
Modeling Language Variation and Universals: a Survey on Typological Linguistics for Natural Language Processing
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing Edoardo Ponti, Helen O ’Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, Thierry Poibeau, Ekaterina Shutova, Anna Korhonen To cite this version: Edoardo Ponti, Helen O ’Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, et al.. Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing. 2018. hal-01856176 HAL Id: hal-01856176 https://hal.archives-ouvertes.fr/hal-01856176 Preprint submitted on 9 Aug 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing Edoardo Maria Ponti∗ Helen O’Horan∗∗ LTL, University of Cambridge LTL, University of Cambridge Yevgeni Berzaky Ivan Vuli´cz Department of Brain and Cognitive LTL, University of Cambridge Sciences, MIT Roi Reichart§ Thierry Poibeau# Faculty of Industrial Engineering and LATTICE Lab, CNRS and ENS/PSL and Management, Technion - IIT Univ. Sorbonne nouvelle/USPC Ekaterina Shutova** Anna Korhonenyy ILLC, University of Amsterdam LTL, University of Cambridge Understanding cross-lingual variation is essential for the development of effective multilingual natural language processing (NLP) applications. -
Urdu Treebank
Sci.Int.(Lahore),28(4),3581-3585, 2016 ISSN 1013-5316;CODEN: SINTE 8 3581 URDU TREEBANK Muddassira Arshad, Aasim Ali Punjab University College of Information Technology (PUCIT), University of the Punjab, Lahore [email protected], [email protected] (Presented at the 5th International. Multidisciplinary Conference, 29-31 Oct., at, ICBS, Lahore) ABSTRACT: Treebank is a parsed corpus of text annotated with the syntactic information in the form of tags that yield the phrasal information within the corpus. The first outcome of this study is to design a phrasal and functional tag set for Urdu by minimal changes in the tag set of Penn Treebank, that has become a de-facto standard. Our tag set comprises of 22 phrasal tags and 20 functional tags. Second outcome of this work is the development of initial Treebank for Urdu, which remains publically available to the research and development community. There are 500 sentences of Urdu translation of religious text with an average length of 12 words. Context free grammar containing 109 rules and 313 lexical entries, for Urdu has been extracted from this Treebank. An online parser is used to verify the coverage of grammar on 50 test sentences with 80% recall. However, multiple parse trees are generated against each test sentence. 1 INTRODUCTION grammars for parsing[5], where statistical models are used The term "Treebank" was introduced in 2003. It is defined as for broad-coverage parsing [6]. "linguistically annotated corpus that includes some In literature, various techniques have been applied for grammatical analysis beyond the part of speech level" [1]. -
Lexical Resource Reconciliation in the Xerox Linguistic Environment
Lexical Resource Reconciliation in the Xerox Linguistic Environment Ronald M. Kaplan Paula S. Newman Xerox Palo Alto Research Center Xerox Palo Alto Research Center 3333 Coyote Hill Road 3333 Coyote Hill Road Palo Alto, CA, 94304, USA Palo Alto, CA, 94304, USA kapl an~parc, xerox, com pnewman©parc, xerox, tom Abstract This paper motivates and describes the morpho- logical and lexical adaptations of XLE. They evolved This paper motivates and describes those concurrently with PARGRAM, a multi-site XLF_~ aspects of the Xerox Linguistic Environ- based broad-coverage grammar writing effort aimed ment (XLE) that facilitate the construction at creating parallel grammars for English, French, of broad-coverage Lexical Functional gram- and German (see Butt et. al., forthcoming). The mars by incorporating morphological and XLE adaptations help to reconcile separately con- lexical material from external resources. structed linguistic resources with the needs of the Because that material can be incorrect, in- core grammars. complete, or otherwise incompatible with The paper is divided into three major sections. the grammar, mechanisms are provided to The next section sets the stage by providing a short correct and augment the external material overview of the overall environmental features of the to suit the needs of the grammar developer. original LFG GWB and its provisions for morpho- This can be accomplished without direct logical and lexicon processing. The two following modification of the incorporated material, sections describe the XLE extensions in those areas. which is often infeasible or undesirable. Externally-developed finite-state morpho- 2 The GWB Data Base logical analyzers are reconciled with gram- mar requirements by run-time simulation GWB provides a computational environment tai- of finite-state calculus operations for com- lored especially for defining and testing grammars bining transducers. -
Wordnet As an Ontology for Generation Valerio Basile
WordNet as an Ontology for Generation Valerio Basile To cite this version: Valerio Basile. WordNet as an Ontology for Generation. WebNLG 2015 1st International Workshop on Natural Language Generation from the Semantic Web, Jun 2015, Nancy, France. hal-01195793 HAL Id: hal-01195793 https://hal.inria.fr/hal-01195793 Submitted on 9 Sep 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. WordNet as an Ontology for Generation Valerio Basile University of Groningen [email protected] Abstract a structured database of words in a format read- able by electronic calculators. For each word in In this paper we propose WordNet as an the database, WordNet provides a list of senses alternative to ontologies for the purpose of and their definition in plain English. The senses, natural language generation. In particu- besides having a inner identifier, are represented lar, the synset-based structure of WordNet as synsets, i.e., sets of synonym words. Words proves useful for the lexicalization of con- in general belong to multiple synsets, as they cepts, by providing ready lists of lemmas have more than one sense, so the relation between for each concept to generate. -
Linguistic Profiles: a Quantitative Approach to Theoretical Questions
Laura A. Janda USA – Norway, Th e University of Tromsø – Th e Arctic University of Norway Linguistic profi les: A quantitative approach to theoretical questions Key words: cognitive linguistics, linguistic profi les, statistical analysis, theoretical linguistics Ключевые слова: когнитивная лингвистика, лингвистические профили, статисти- ческий анализ, теоретическая лингвистика Abstract A major challenge in linguistics today is to take advantage of the data and sophisticated analytical tools available to us in the service of questions that are both theoretically inter- esting and practically useful. I offer linguistic profi les as one way to join theoretical insight to empirical research. Linguistic profi les are a more narrowly targeted type of behavioral profi ling, focusing on a specifi c factor and how it is distributed across language forms. I present case studies using Russian data and illustrating three types of linguistic profi ling analyses: grammatical profi les, semantic profi les, and constructional profi les. In connection with each case study I identify theoretical issues and show how they can be operationalized by means of profi les. The fi ndings represent real gains in our understanding of Russian grammar that can also be utilized in both pedagogical and computational applications. 1. Th e quantitative turn in linguistics and the theoretical challenge I recently conducted a study of articles published since the inauguration of the journal Cognitive Linguistics in 1990. At the year 2008, Cognitive Linguistics took a quanti- tative turn; since that point over 50% of the scholarly articles that journal publishes have involved quantitative analysis of linguistic data [Janda 2013: 4–6]. Though my sample was limited to only one journal, this trend is endemic in linguistics, and it is motivated by a confl uence of historic factors. -
Morphological Processing in the Brain: the Good (Inflection), the Bad (Derivation) and the Ugly (Compounding)
Morphological processing in the brain: the good (inflection), the bad (derivation) and the ugly (compounding) Article Published Version Creative Commons: Attribution 4.0 (CC-BY) Open Access Leminen, A., Smolka, E., Duñabeitia, J. A. and Pliatsikas, C. (2019) Morphological processing in the brain: the good (inflection), the bad (derivation) and the ugly (compounding). Cortex, 116. pp. 4-44. ISSN 0010-9452 doi: https://doi.org/10.1016/j.cortex.2018.08.016 Available at http://centaur.reading.ac.uk/78769/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . To link to this article DOI: http://dx.doi.org/10.1016/j.cortex.2018.08.016 Publisher: Elsevier All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online cortex 116 (2019) 4e44 Available online at www.sciencedirect.com ScienceDirect Journal homepage: www.elsevier.com/locate/cortex Special issue: Review Morphological processing in the brain: The good (inflection), the bad (derivation) and the ugly (compounding) Alina Leminen a,b,*,1, Eva Smolka c,1, Jon A. Dunabeitia~ d,e and Christos Pliatsikas f a Cognitive Science, Department of Digital Humanities, Faculty of Arts, University of Helsinki, Finland b Cognitive Brain Research -
Inflection), the Bad (Derivation) and the Ugly (Compounding)
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Central Archive at the University of Reading Morphological processing in the brain: the good (inflection), the bad (derivation) and the ugly (compounding) Article Published Version Creative Commons: Attribution 4.0 (CC-BY) Open Access Leminen, A., Smolka, E., Duñabeitia, J. A. and Pliatsikas, C. (2019) Morphological processing in the brain: the good (inflection), the bad (derivation) and the ugly (compounding). Cortex, 116. pp. 4-44. ISSN 0010-9452 doi: https://doi.org/10.1016/j.cortex.2018.08.016 Available at http://centaur.reading.ac.uk/78769/ It is advisable to refer to the publisher's version if you intend to cite from the work. See Guidance on citing . To link to this article DOI: http://dx.doi.org/10.1016/j.cortex.2018.08.016 Publisher: Elsevier All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading's research outputs online cortex 116 (2019) 4e44 Available online at www.sciencedirect.com ScienceDirect Journal homepage: www.elsevier.com/locate/cortex Special issue: Review Morphological processing in the brain: The good (inflection), the bad (derivation) and the ugly (compounding) Alina Leminen a,b,*,1, Eva Smolka c,1, Jon A. Dunabeitia~ d,e and Christos Pliatsikas -
Modeling and Encoding Traditional Wordlists for Machine Applications
Modeling and Encoding Traditional Wordlists for Machine Applications Shakthi Poornima Jeff Good Department of Linguistics Department of Linguistics University at Buffalo University at Buffalo Buffalo, NY USA Buffalo, NY USA [email protected] [email protected] Abstract Clearly, descriptive linguistic resources can be This paper describes work being done on of potential value not just to traditional linguis- the modeling and encoding of a legacy re- tics, but also to computational linguistics. The source, the traditional descriptive wordlist, difficulty, however, is that the kinds of resources in ways that make its data accessible to produced in the course of linguistic description NLP applications. We describe an abstract are typically not easily exploitable in NLP appli- model for traditional wordlist entries and cations. Nevertheless, in the last decade or so, then provide an instantiation of the model it has become widely recognized that the devel- in RDF/XML which makes clear the re- opment of new digital methods for encoding lan- lationship between our wordlist database guage data can, in principle, not only help descrip- and interlingua approaches aimed towards tive linguists to work more effectively but also al- machine translation, and which also al- low them, with relatively little extra effort, to pro- lows for straightforward interoperation duce resources which can be straightforwardly re- with data from full lexicons. purposed for, among other things, NLP (Simons et al., 2004; Farrar and Lewis, 2007). 1 Introduction Despite this, it has proven difficult to create When looking at the relationship between NLP significant electronic descriptive resources due to and linguistics, it is typical to focus on the dif- the complex and specific problems inevitably as- ferent approaches taken with respect to issues sociated with the conversion of legacy data. -
TEI and the Documentation of Mixtepec-Mixtec Jack Bowers
Language Documentation and Standards in Digital Humanities: TEI and the documentation of Mixtepec-Mixtec Jack Bowers To cite this version: Jack Bowers. Language Documentation and Standards in Digital Humanities: TEI and the documen- tation of Mixtepec-Mixtec. Computation and Language [cs.CL]. École Pratique des Hauts Études, 2020. English. tel-03131936 HAL Id: tel-03131936 https://tel.archives-ouvertes.fr/tel-03131936 Submitted on 4 Feb 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Préparée à l’École Pratique des Hautes Études Language Documentation and Standards in Digital Humanities: TEI and the documentation of Mixtepec-Mixtec Soutenue par Composition du jury : Jack BOWERS Guillaume, JACQUES le 8 octobre 2020 Directeur de Recherche, CNRS Président Alexis, MICHAUD Chargé de Recherche, CNRS Rapporteur École doctorale n° 472 Tomaž, ERJAVEC Senior Researcher, Jožef Stefan Institute Rapporteur École doctorale de l’École Pratique des Hautes Études Enrique, PALANCAR Directeur de Recherche, CNRS Examinateur Karlheinz, MOERTH Senior Researcher, Austrian Center for Digital Humanities Spécialité and Cultural Heritage Examinateur Linguistique Emmanuel, SCHANG Maître de Conférence, Université D’Orléans Examinateur Benoit, SAGOT Chargé de Recherche, Inria Examinateur Laurent, ROMARY Directeur de recherche, Inria Directeur de thèse 1. -
Unification of Multiple Treebanks and Testing Them with Statistical Parser with Support of Large Corpus As a Lexical Resource
Eng. &Tech.Journal, Vol.34,Part (B), No.5,2016 Unification of Multiple Treebanks and Testing Them With Statistical Parser With Support of Large Corpus as a Lexical Resource Dr. Ahmed Hussein Aliwy Computer Science Department, University of Kufa/Kufa Email:[email protected] Received on:12/11/2015 & Accepted on:21/4/2016 ABSTRACT There are many Treebanks, texts with the parse tree, available for the researcher in the field of Natural Language Processing (NLP). All these Treebanks are limited in size, and each one used private Context Free Grammar (CFG) production rules (private formalism) because its construction is time consuming and need to experts in the field of linguistics. These Treebanks, as we know, can be used for statistical parsing and machine translation tests and other fields in NLP applications. We propose, in this paper, to build large Treebank from multiple Treebanks for the same language. Also, we propose to use an annotated corpus as a lexical resource. Three English Treebanks are taken for our study which arePenn Treebank (PTB), GENIA Treebank (GTB) and British National Corpus (BNC). Brown corpus is used as a lexical resource which contains approximately one million tokens annotated with part of speech tags for each. Our work start by the unification of POS tagsets of the three Treebank then the mapping process between Brown Corpus tagset and the unified tagset is done. This is done manually according to our experience in this field. Also, all the non-terminals in the CFG production are unified.All the three Treebanks and the Brown corpus are rebuilt according to the new modification. -
Instructions for ACL-2010 Proceedings
Towards the Representation of Hashtags in Linguistic Linked Open Data Format Thierry Declerck Piroska Lendvai Dept. of Computational Linguistics, Dept. of Computational Linguistics, Saarland University, Saarbrücken, Saarland University, Saarbrücken, Germany Germany [email protected] [email protected] the understanding of the linguistic and extra- Abstract linguistic environment of the social media post- ing that features the hashtag. A pilot study is reported on developing the In the light of recent developments in the basic Linguistic Linked Open Data (LLOD) Linked Open Data (LOD) framework, it seems infrastructure for hashtags from social media relevant to investigate the representation of lan- posts. Our goal is the encoding of linguistical- guage data in social media so that it can be pub- ly and semantically enriched hashtags in a lished in the LOD cloud. Already the classical formally compact way using the machine- readable OntoLex model. Initial hashtag pro- Linked Data framework included a growing set cessing consists of data-driven decomposition of linguistic resources: language data i.e. hu- of multi-element hashtags, the linking of man-readable information connected to data ob- spelling variants, and part-of-speech analysis jects by e.g. RDFs annotation properties such as of the elements. Then we explain how the On- 'label' and 'comment' , have been suggested to toLex model is used both to encode and to en- be encoded in machine-readable representation3. rich the hashtags and their elements by linking This triggered the development of the lemon them to existing semantic and lexical LOD re- model (McCrae et al., 2012) that allowed to op- sources: DBpedia and Wiktionary.