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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
BulTreeBank Project
LML, Bulgarian Academy of Sciences
(www. bultreebank.org)
Workshop on Balkan Language Resources and Tools 2003
21 November 2003 Thessaloniki, Greece
Plan of the talk
• Preliminary Notes
• BulTreeBank Language Resources and Tools
• The integration architecture of the resources and tools
• Conclusion and Future work
Financial Support
BulTreeBank is a joint project betweenSeminar für Sprachwissenschaft,
Eberhard-Karls-Universität, Tübingen, Germanyand
Linguistic Modelling Laboratory,Bulgarian Academy of Sciences, Sofia, Bulgaria
The project is funded by the Volkswagen-Stiftung, Germany
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
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
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
Language Resources
• Text archive
• Morphological dictionary
• Gazetteers
• Valence dictionary
• Semantic dictionary
• Treebank
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
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)
The Gazetteers
• Gazetteers of namesconsisting 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
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)
Lexical Entry of the Valence Dictionary
Verb, its transitivity and aspectMeaningI. Frame (the arguments that the verb requires)
S(ubject) + P(redicate) + O2(indirect object) | C(lause)
II. Morphology of the verb's argumentsS(ubject)=N,PerPron
III. Semantics of the argumentsS(ubject) is a person
IV. Examples of the verb's usage
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
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
Core set of sentences: Example of a Pragmatic Adjunct
A Corpus Sentence: an example of dependents realisation
The Tools
• Morphological analyzer
• Disambiguator(s)
• Partial grammars
– sentence splitter
– named-entity recognition module
– chunkers
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
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
After the MorphoSyntactic Analysis and Disambiguation
<w><ph>Човек</ph> <aa>Ncmsi</aa><ta>Ncmsi</ta></w><w><ph>с</ph> <aa>R</aa><ta>R</ta></w><w><ph>опит</ph> <aa>Ncmsi;Vppt+cv--smi</aa><ta>Ncmsi</ta></w><w><ph>и</ph> <aa>C</aa><ta>C</ta></w><w><ph>богато</ph> <aa>Ansi;D</aa><ta>Ansi</ta></w><w><ph>минало</ph> <aa>Ansi;Ncnsi;Vppt+caosni</aa><ta>Ncnsi</ta></w>
Named-entity recognition
Based on the information from the gazetteers and on RE rules:
• numerical expressions
• names
• abbreviations
• special symbols
After the application of Gazetteers
<np sort="NE-Org"> <w><ph>Бъдеще</ph><ta>Ncnsi</ta></w> <pp> <w><ph>за</ph><ta>R</ta></w> <w
sort="NE-Loc"><ph>България</ph><ta>Ncfsi</ta></w> </pp></np>
<np sort="NE-Pers"> <w>Димитър</w><w>Калчев</w></np>
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
Chunkers
• NP chunker – after preposition NPs– “sure” non-recursive NPs
• VP chunker– Analytical wordforms– “Da” constructions– Verb clitics
• PP chunker, AP chunker, Clausal chunker
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
Integration of the resources and tools
• The order of application
• Mutual dependence
• Quantitative and qualitative expansion
The principle of cascadedness
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
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