What is a national corpus

Primary objective of a national corpus is to provide linguists with a tool to investigate a language in the diversity of types of texts through making complex lexical grammatical queries.

The corpus allows to investigate various linguistic phenomena by observing the possible range of contexts in which they occur.

Examples of searchable corpora online

British National Corpus

Russian National Corpus

Eastern Armenian National Corpus

Czech National Corpus

To show just one example:Eastern Armenian National Corpus

• about 90 million tokens • powerful search engine for making complex lexical morphological queries • a diachronic corpus covering SEA texts from the mid-19th century to the present • both written discourse and oral discourse • open access

A national corpus is a large-scale, linguistically diversified and balanced collection of texts provided with a flexible search engine.

How large?

RNC 150 mlnBNC 100 mlnEANC 90 mln

Essentially, depends on the type of research envisaged

How diversified?

As diversified as practicable

EANC – extension of the press subcorpus to cover early Armenian press, soon to cover internet forums

RNC – effort to cover snail mail and electronic communication

EANC: subcorpus form

How balanced?

Balance is a vague notion…

At least not disproportionate – less poetry than prose etc. Even a disbalanced corpus can be balanced by creating predefined subcorpora.

As an example: EANC

Written discourse # tokens % EANC # of docs

Fiction

prose: novel 23 487 427 32,0% 287

prose: story 5 203 507 7,1% 104

prose: play 1 407 344 1,9% 46

prose subtotal 30 098 278 41,0% 437

poetry 2 392 710 3,3% 106

Press 22 471 921 30,6% 3895

Nonfiction

science 13 354 755 18,2% 109

essays, memoirs, official, religious 3 894 015 5,3% 320

Written discourse total 72 211 679 98,5% 4 867

Multicomponent corpora

Oral subcorpus (RNC, BNC, EANC)Dialectal subcropus (RNC)Poetic subcropus (RNC)Educational subcorpus (RNC)…

Library or corpus?

• electronic library is intended for readers

• corpus is intended for researchers

Difference in target audience and intended usage

Implied differences:

corpus must be able to respond to queries

library have major problems related to copyright

Technical requirement: reasonable expectation time

Functional requirement: complex queries

• you can not parse texts as you go (on flight)

texts need to contain mark up

• in large corpora, you can not simply search the markup

you have to index files, create datafiles and use special search algorythms

Parsing

Сlassification of inflectional types needs to be as exhaustive and formal as a logical calculus.

Parser creates a list of endings and a list of stems; when parsing a wordform, it tries to match the ending of the word with an ending in the list, then tries to match the rest with the stem, and checks whether this ending is allowed to be added to this stem.

• wordlist

• inflection type attributed to its each item

Parsing

•recent loanwords •neologisms•elements of code-

switching•abbreviations•proper names •technical terms

•distorted spellings•cases of inflectional variance

not included into the wordlist•scanning errors•typos and misspellings in the

original texts

Some tokens are not recognized at all; these tokens can not be searched by means of lexical or grammatical queries.

Parsing

Some tokens receive several analyses.

The actual applicability of these analyses depend on the context and may not be evaluated by the parser.

# of analyses Comment Fiction Science PressOther

WrittenOral

DiscourseEANC Total

1 unambiguous 73,9% 65,9% 70,4% 68,0% 63,0% 70,9%

2 ambiguous (homonimous) 15,4% 9,8% 12,4% 12,3% 14,1% 13,2%

3 ambiguous (homonimous) 2,7% 2,0% 1,9% 3,8% 2,4% 2,3%

4 - 7 ambiguous (homonimous) 1,4% 1,8% 1,8% 1,6% 1,5% 1,6%

Subtotal ambiguous 19,5% 13,7% 16,0% 17,7% 18,0% 17,1%

1? hypothetic (not in dictionary) 0,0% 1,3% 0,6% 0,7% 0,2% 0,5%

               

0 not recognized 6,2% 12,8% 9,9% 8,0% 13,9% 8,9%

Special tokens: Cyrillic, Latin, digits 0,3% 6,3% 3,1% 5,6% 4,9% 2,6%

Total   100% 100% 100% 100% 100% 100%

Search Functionality

Once again: the Corpus allows to investigate various linguistic phenomena by observing the range of contexts in which they occur.

• token queries

• context queries

• subcorpus queries

Search Functionality

Simple token queries:

• lexeme search

• wordform search

• gram search

Combined token queries:

• lexeme + gram search

Search Functionality

Additional and advanced options for token queries:

• case-sensitivity

• punctuation marks

• position in the sentence

• wildcard queries

• logical functions

• negated features

Search Functionality

Context queries: a combination of several token queries

• search for tokens at a specified distance

• search for tokens within one sentence

• search for tokens in adjacent sentences

• increasing the number of tokens ad infinitum

Search Functionality

Subcorpus selection: searching in a specified type of texts only

• search within a specific period of time

• search in texts of specified authors

• search in specified genres/types of texts

Search Functionality

Working with the results

• expanding the context

• pop-up grammar

• sort by…

Extras

• Translations (EANC)• Disambiguation (RNC)• Electronic library (EANC)• Syntactic markup• Statistics (RNC?)

Possible applications

Linguistics(corpus-based grammars projects under way) Education (www.studiorum.ruscorpora.ru to appear) Normative linguistics Literature and culture studies etc.