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Language Resources and Tools for the Creation of a Bulgarian Treebank Kiril Simov, Petya Osenova, Sia Kolkovska, Elisaveta Balabanova, Dimitar Doikoff.

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Presentation on theme: "Language Resources and Tools for the Creation of a Bulgarian Treebank Kiril Simov, Petya Osenova, Sia Kolkovska, Elisaveta Balabanova, Dimitar Doikoff."— Presentation transcript:

1 Language Resources and Tools for the Creation of a Bulgarian Treebank Kiril Simov, Petya Osenova, Sia Kolkovska, Elisaveta Balabanova, Dimitar Doikoff BulTreeBank Project LML, Bulgarian Academy of Sciences (www. Workshop on Balkan Language Resources and Tools 2003 21 November 2003 Thessaloniki, Greece

2 Plan of the talk Preliminary Notes BulTreeBank Language Resources and Tools The integration architecture of the resources and tools Conclusion and Future work

3 Financial Support BulTreeBank is a joint project between Seminar für Sprachwissenschaft, Eberhard-Karls-Universität, Tübingen, Germany and Linguistic Modelling Laboratory, Bulgarian Academy of Sciences, Sofia, Bulgaria The project is funded by the Volkswagen-Stiftung, Germany

4 Expected Results A set of Bulgarian sentences marked-up with detailed syntactic information A core set of sentences designated inside the treebank A linguistically interpreted text archive for Bulgarian A reliable partial grammar for automatic parsing of phrases in Bulgarian Software modules for compiling, manipulating and exploring the language resources

5 Preliminary notes (1) We rely on two prerequisites during the process of our treebank creation: – integration of the pre-processing components –an adequate annotation scheme

6 Preliminary notes (2) Integration is performed with the help of the following techniques: –Looking-forward strategy Adaptive mechanism Additive mechanism –Looking-backward strategy –Creation of a gold standard

7 Language Resources Text archive Morphological dictionary Gazetteers Valence dictionary Semantic dictionary Treebank

8 The BulTreeBank Text Archive A collection of linguistically interpreted texts from different genres (target size: 100 million words) About 72 million running words are converted into XML documents, marked up in conformance with the TEI guidelines 10 million running words are morphologically analyzed Over 1 000 000 words are morphosyntactically disambiguated by hand

9 The morphological dictionary Published as a book – Popov, Simov and Vidinska, 1998 It covers the grammatical information of about 100 000 lexemes (1 600 000 word forms) and serves as a basis for the morphological analyzer The problem of the unknown words: open classes (names, abbreviations) and derivational models (diminutives etc)

10 The Gazetteers Gazetteers of names consisting of 15 000 words – Bulgarian and foreign person names, locations from the whole world, organizations, and others Gazetteers of the most frequent abbreviations consisting of 1500 acronyms and graphical abbreviations Gazetteers of 300 most frequent introductory expressions and parentheticals. This is considered to be a step towards a basic list of collocations

11 The Valence Dictionary It consists of 1000 verbs and their valence frames The frames of the most frequent verbs are compared to the corpus data and repaired if necessary (new frames added, old ones deleted or more fine-grained) The semantic restrictions over the arguments are extracted and matched against the SIMPLE ontology (recall the Semantic Dictionary)

12 Lexical Entry of the Valence Dictionary Verb, its transitivity and aspect Meaning I.Frame (the arguments that the verb requires) S(ubject) + P(redicate) + O2(indirect object) | C(lause) II.Morphology of the verb's arguments S(ubject)=N,PerPron III.Semantics of the arguments S(ubject) is a person IV.Examples of the verb's usage

13 The Semantic Dictionary Classification of the most frequent nouns with respect to the ontological hierarchy of SIMPLE without specifying the synonymic relations between them (3 000 nouns) The proper names from the gazetteers are also mapped to the ontological hierarchy of SIMPLE

14 The Treebank Core set of sentences (1 500 sentences) - extracted mainly from Bulgarian grammars and processed manually --> highest quality Treebank (6 000 sentences) - extracted mainly from the corpus and pre-processed automatically before treated manually

15 Core set of sentences: Example of a Pragmatic Adjunct

16 A Corpus Sentence: an example of dependents realisation

17 The Tools Morphological analyzer Disambiguator(s) Partial grammars –sentence splitter –named-entity recognition module –chunkers

18 Morphological Analyzer Assigns all possible analyses to the tokens Implemented in CLaRK System as a regular grammar Works together with the ‘token classification’ strategy and with the gazetteers

19 Disambiguator(s) Rule-based disambiguator - a preliminary version of a rule-based morpho-syntactic disambiguator, encoded as a set of constraints within the CLaRK system --> 80 % coverage Neural-network-based disambiguator (Simov and Osenova 2001). Its accuracy is of 95.25 % for part-of-speech and 93.17 % for complete morpho- syntactic disambiguation

20 After the MorphoSyntactic Analysis and Disambiguation Човек Ncmsi Ncmsi с R R опит Ncmsi;Vppt+cv--smi Ncmsi и C C богато Ansi;D Ansi минало Ansi;Ncnsi;Vppt+caosni Ncnsi

21 Named-entity recognition Based on the information from the gazetteers and on RE rules: numerical expressions names abbreviations special symbols

22 After the application of Gazetteers Бъдеще Ncnsi за R България Ncfsi Димитър Калчев

23 Chunkers: General Assumptions Deals with non-recursive constituents Relies on a clear-indicator strategy Delays the attachment decisions Ignores semantic information Aims at accuracy, not coverage

24 Chunkers NP chunker – after preposition NPs –“sure” non-recursive NPs VP chunker –Analytical wordforms –“Da” constructions –Verb clitics PP chunker, AP chunker, Clausal chunker

25 After the application of some Chunk Grammars Common NP chunks –[един човек] от [града] (‘one man from town-the’) Name NP chunks: NEpers, NEloc etc. –[Министерство на културата] (‘Ministry of Culture’) Complex NP chunks –[нашето [Министерство на културата]] (‘our Ministry of Culture’) Analytical verb forms –[да [му я даде]] (‘to him her give-3p, sg’) to give it to him

26 Integration of the resources and tools The order of application Mutual dependence Quantitative and qualitative expansion The principle of cascadedness

27 Conclusion We described a set of basic language resources which are necessary for the creation of a Bulgarian treebank We outlined our tasks in the context of a ‘less- processed’ language (variety and flexibility of LRs and tools) It was shown that the creation of one type of resource (in our case - the treebank) can evoke the successful creation of other types of resources

28 Future tasks using the LRs and tools as separate modules for applications like Information retrieval and Extraction to extend the basic language resources into a more elaborate set, richer in information and relations to continue testing and validating the resources to invest more in their evaluation

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