treebanks, linguistic theories and...

30th European Summer School in Logic, Language and Information (6 August – 17 August 2018)

Petya Osenova and Kiril Simov Sofia University “St. Kliment Ohridski”, Bulgaria

Bulgarian Academy of Sciences, BulgariaESSLLI 2018

Treebanks, Linguistic Theories and ApplicationsAnnotation Schemes

Lecture Two


Plan of the Lecture

• Motivation for Text Annotation• General Architecture of the Annotation• Participants in the Annotation Flow• Characteristics of the Annotation

2


Motivation

Syntactically Annotated Texts enable the production of:• Linguistic Research• Parsers: high-quality; able to parse large amounts of texts

and texts in various languages making them structured• Information Extraction: getting relevant information from

texts, based not only on strings but also on structures• More precise Machine Translation• and much more

3


General Annotation Architecture• Selection of texts (wrt to some task)• Selection of the Annotation Tool• Design of an Annotation Scheme wrt Linguistic Theory• Training Annotators• Annotation

• measuring interannotator agreement• juridication

• Re-Annotation• Validation

4


Participants in the Annotation Cycle

• Annotation Scheme Designer(s)• Annotation Tool creator/maintainer• Annotator Trainer• Annotator(s)• Juridicator• User(s)

5


The BulTreeBank Experience

• The treebank as a ‘botanical garden’

• The parsebank with error checking as a ‘forest’

• The cashbank with automatically parsed texts as a ‘jungle’

6


BulTreeBank Experience: Goals

• A set of Bulgarian sentences marked-up with detailed syntactic information

• A core set of sentences designated inside the treebank • Reliable partial grammar for automatic parsing of phrases in

Bulgarian• Software modules for compiling, manipulating and

exploring the treebank

7


Requirements

• Adequate representation of the linguistic facts – Theory dependency

• Adequate representation of partial and complete analysis– Easy transfer of the information

• Convenience for manual annotation– Minimal information input

8


Why Theory Dependency? (1)

• On a certain level of granularity the annotation scheme becomes very complicated to be processed consistently

• On a certain level of granularity some linguistic theory has to be exploited

• Two choices:– A new “annotation” linguistic theory to be developed, or– A well-established existing theory to be adopted

We have chosen HPSG as a base for our treebank

9


Why Theory Dependency? (2)

• HPSG is one of the major linguistic theories based on rigorous formal grounds

• HPSG allows for a consistent description of linguistic facts on every linguistic level: syntactic, semantic and others

• HPSG allows for different levels of generalisation and therefore enables different experts to work on different levels of analysis

• The formal basis of HPSG allows translation to other formalisms• There are universal HPSG principles that can be used to support the

work of the annotators

10


Core Set of Sentences

In the process of the treebank compilation it plays double role– Gold standard: this set has to cover the basic linguistic

phenomena in Bulgarian– HPSG Grammar development basis

Here we present and discuss the annotation scheme for the treebank

11


HPSG Language Model

• Linguistic objects– Represented as directed graphs (feature structures)

• Sort hierarchy (linguistic ontology)– Represents the main types of linguistic objects and their

characteristics• Grammar (theory)

– HPSG Universal and Bulgarian Specific Principles– Bulgarian Lexicon

12


HPSG Linguistic Objects

• The main linguistic object is of sort sign with three main attributes: PHON, SYNSEM and DTRS (for phrases)

• The co-reference (structure sharing) is the basic mechanism for ensuring the correct object structure

• The attribute DTRS determines the variety of constituent structures and the grammatical functions

13


The Hierarchy of Phrasesheaded-phrase

head-complementhead-subjecthead-adjunct

head-sem-adjuncthead-pragmatic-adjunct

head-fillernon-headed-phrase

14


Constituency and Dependency (1)

• HPSG separates the linear order from the constituent structure

• Each constituent structure reflects the dependency between its immediate constituents

• The realization of the dependants follows the sequence:complements > subject > adjuncts

15


Constituency and Dependency (2)

16

Support-the

of

readinesssoldierof

includes

restriction

of

his freedom


Linguistic Object Representation

The representation of the linguistic objects (of sort sign) in the core set of sentences is based on:

• Context-free-like trees• Coreferencial relations over the trees• Node labels reflecting the synsem information

17


Word Order and Discontinuity

• Continuous realisation of daughters • Head dependants permutation

a constituent from an upper level of the hierarchy is realised between constituents of a lower level

• Mixture of two saturated constituentsthe constituents of two saturated phrases are mixed with each other

• External realisation of an inner constituentextraction

18


Head Dependants Permutation

Мъжът целува момичето.Man-the kisses girl-the.Целува момичето мъжът.Kisses girl-the man-the.Момичето целува мъжът.Girl-the kisses man-the.

Мъжът момичето целува.Man-the girl-the kisses.Момичето мъжът целува.Girl-the man-the kisses.

19


Head Dependants Permutation

man-the

girl-the kisses

20


External Realisation of an Inner Constituent

На къщата той поправиOf house-the he repaired

покриваroof-the

He repaired the house roof.Of house-the repaired

he

roof-the21


Mixture of Two Saturated Constituents

малки го young it.MASC

моми беряхаgirls pick.IMPERF

young girls were picking ityoung it girls were picking

22


Realisation of the Dependants

With this case I was acquainted in time

23

With

this case

I

was

in time

acquainted


Linguistic Parameters• We rely on two basic assumptions:

– We use a domain-phenomena cross-classification, where the main syntactic domains are defined and the phenomena are analyzed

– We analyze the data according to the following HPSG-oriented criteria:

• the type of the sign, • headedness, • the typology of words and phrases, • the saturation condition

24


Core Domains: NP

• Bare Bulgarian NPs are always functionally complete (lexical category N)

• NP dependency structures: head-complement (NPC), head-adjunct (NPA)

• Classification criteria: ontological features (mainly for the named entities), ellipsis, substantivization, nominalization

25


Core Domains: VP (1)

• VPs are classified as lexical and phrasal• The lexical (V) includes:

– Bare verbs– Verbs with clitics– Da-constructions– Analytical verb forms– Elliptical verbs

26


Core Domains: VP (2)

• The phrasal category is recursive: – First, the verb with its full-fledged complement(s) forms a

head-complement phrase (VPC) – Then, the head-complement VP takes the subject and forms a

head-subject phrase (VPS)– The adjuncts are attached last and form head-adjunct (VPA) projections– Each extracted element without a structural parent is attached to a

head-filler phrase (VPF)– CL stands for a saturated verb phrase

27


Da Clause Example

Не може да не сме сеNot may to not to be.PL.1P REFLEX-PART

разминали на някой светофар.Pass.PRES.PERF on some traffic lights.

It is not possible that we have passed each other at some traffic light.

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Da Clause

29

It is not possible that we have not passed each other at some traffic light.

Not possible

to

not

are

REFLEX-PART

passed each other

of

some traffic light


Core Domains: AP, AdvP, PP

• Lexical adjective (A) can be combined with possessive clitic• AP can be head-complement phrase (APC), and head-adjunct

phrase (APA)• Non-modified adverb is marked lexically (Adv) • AdvP can be head-adjunct phrase (AdvPA) and head-complement

with a gerund head (AdvPC)• PP is always a head-complement phrase

30


Coordination

Coordination is treated as a non-headed phrase with the following requirements:

• The conjuncts have to agree in their valency potential: Valency lists and Mod feature

• They can be underspecified with respect to the category: coord

31


Lexical Coordination

Тя умееше да приготви и натъкмиShe knows.IMPERF to prepare and adjust

всичко много добре. everything very well.

She knew how to prepare and adjust everything very well.

32


LexicalCoordination

33

She knew how to prepare and adjust everything very well.

She

knew

to

prepare and adjust

everything very well


Clausal (1)Той щеше да стои в Копривщица и He would to stay in Koprivshtitsa (town) and

да се обучава в стрелба.to REFL.PART train in shooting.

He would stay in Koprivshtitsa and be trained in shooting.

34


Clausal

35

He would stay in Koprivshtitsa and be trained in shooting.

He

would

to stay in Koprivshtitsa to be trained

in shootingREFLEX-PART

and


Clausal (2)Момчето хвана стареца за ръка и Boy-the took old man-the for hand and

го поведе.him.MASC led.

The boy took the old man by the hand and led him.

36


Clausal

37

The boy took the old man by the hand and led him.

Boy-the

took old man

by hand

and

him led


NP Internal

В тишина се носеше равният иIn silence REFL.PART spread even-the and

самотен глас на кукувицата.lonely voice of cuckoo-the.

The monotonic and lonely voice of the cuckoo was floating in the silence.

38


NP Internal

39

The monotonic and lonely voice of the cuckoo was floating in the silence.

In silence

floatingREFLEX-PART

monotonic and lonely

voice of cuckoo


NP Coordination

измамата и плагиатството са незаконниcheating-the and plagiarism-the are illegal

The cheating and the plagiarism are illegal.

40


NP Coordination

41

The cheating and the plagiarism are illegal.

cheating and plagiarism

are illegal


Adjunct Coordination – Unlike Categories

Тя отиде дома бързо и She went home quickly and

развълнувана като младо момиче.еxcited.FEM as young girl.

She went home quickly and excited as a young girl.

42


AdjunctCoordination (Unlike Categories)

43

She went home quickly and excited as a young girl.

She

went home quickly and

excited

as

young girl


Pragmatic constituents

Elements of the sentences structure with primarily pragmatic impact

Here we include different kinds of parenthetical expressions (of course, on the other hand, etc), vocative phrases

They are attached to the phrases which they modify pragmatically as adjuncts

44


Pragmatic Adjunct

Освен тебе, мале, никого нямам.Except you mother nobody not-have-I.

Besides you, mother, I have nobody.

45


Pragmatic Adjunct

46

Besides you, mother, I have nobody.

Besides you mother nobody do not have-I


Preference Rules• Coordination

Prefer constituent coordination to ellipsis!• Ellipsis

If in the sentence there is an anchoring element for the ellipsis restoration, prefer it to the discourse one!

• Modal verbsIn sentences with two readings possible: personal and impersonal,

prefer the personal reading!

47


Core Phenomena (1)Unexpressed Elements:• Pro-dropness

[kazah mu] [da prochete knigata]‘I told him to read the book.’

• Ellipsis[Ivan pie bira,] [a Maria vino]‘John drinks beer, but Maria wine.’

• Frame alternation[kazah mu] [da chete]‘I told him to read.’

48


Core Phenomena (2)

Co-referential Relations:• Agreement• Binding• Anaphora resolution• Definiteness• Control

49


Core Phenomena (3)• Relative clauses• Secondary predication

Type-shifting:

• Substantivization• Nominalization

50


Secondary Predication

Планинският вятър духаше вече Mountain wind blow.IMPERF already

доста свеж и силен.very fresh and strong.

The mountain wind was already blowing quite fresh and strong.

51


Secondary Predication

52

The mountain wind was already blowing quite fresh and strong.

mountain wind

blowing

already quite

fresh and strong


Substantivization

Заговори бързо, защото младите Start-to-speak.PAST quickly because young-the

приближаваха.come-near.PAST

He spoke quickly, because the young were coming.

53


Substantivization

54

He spoke quickly, because the young were coming.

Spoke quickly

because

young

coming


Verb Ellipsis

Вол се връзва за рогата, Ox REFLEX-PART tied for horns, а човек за езика.but person for tongue

Ox is tied on its horns while person [is tied] by its tongue.

55


Verb Ellipsis

Ox is tied on its horns while person [is tied] by its tongue.

Ox

REFLEX-PART tied

for horns person for tongue

56


Verb Ellipsis – 2 (with Polarity Change)

Нямаше заплаха, а само радостWas-no thread, but only joy

There was no thread, but only joy.

57


Verb Ellipsis – 2 (with Polarity Change) There was no thread, but [there was] only joy.

change from negative to positive polarity for the elided verb

there was no thread

only joy

there was

58


Noun Ellipsis

Българското и гръцкото правителствоBulgarian-the and Greek-the government

The Bulgarian and the Greek governments.

59


Noun Ellipsis

The Bulgarian and the Greek governments.

Bulgarian Greek government

and

60


More Ellipses

Радиацията в Косово била Radiation-the in Kosovo was-RENARRATIVE

по-слаба от Софияmore weak from Sofia

They say that radiation in Kosovo was weaker than in Sofia.

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More Ellipses

They say that radiation in Kosovo was weaker than [the radiation] in Sofia.

Radiation was

in Kosovo weaker

from(than)

Sofia62


Annotation Process (1)

• Sentence extraction• Automatic pre-processing

– Morphosyntactic tagging– Part-of-speech disambiguation– Partial parsing– Adding syntactic information from the HPSG grammar (Ideally

automatic)• Manual annotation

– XML mark-up and constraints over XML documents

63


Annotation Process (2)We use two systems:• CLaRK system for:

– Language resource creation, management and exploration– Minimisation of human work– Facilities for semantic validation of the information in language

resources (content checking in addition to structure checking)• TRALE system for:

– Representation of the HPSG Grammar– For logical inference

64


Annotation Process Example (1)Sentence extraction (after morphological analysis): The pretty

girl listened to the boy’s short story.

65

pretty girl listened short story of boy


Annotation Process Example (2)

Automatic pre-processing (after the partial parsing)

66

pretty girl

listened

short story

of boy


Annotation Process Example (3)Adding syntactic information from the HPSG grammar (1)

67

pretty girl listened

short story of boy


Annotation Process Example (4)Adding syntactic information from the HPSG grammar (2)

68

pretty girl listened

short story

of boy


Annotation Process Example (5)Manual annotationThe intersection of all possible analyses is calculated:

69

pretty girl

listened

short story of boy

The information from the possible analyses, which is outside the intersection, is encoded as constraints over this document


XML Implementation

Two basic principles accepted during the DTD design:– the XML tree model is used to represent as much as possible from

the structure of the sentence analysis– the order of the lexical elements corresponds to the word order in

the sentence – no empty elements are inserted in the structure (no traces)

70


XML Elements

• Structural elements corresponding to the linguistic domains, additionally classified wrt their sign and constituent structure:– Lexical level: V, N, A, … elements– Phrasal level: VPC, VPS, VPA, NPC, NPA, …

• Discontinuous elements: DiscA, DiscE, …

71


Example of a Partial DTD Declaration <!ENTITY % vpcomp

"N|Subst|Nomin|Pron|NPC|NPA|A|APA|APC|PP|Adv|AdvPA|%CLALL;" >

<!ENTITY % vpchead "VD-Elip|V|V-Elip|Participle|Verbalised" >

<!ELEMENT VPC (( (%vpchead;), (%vpcomp;)+ ) |( (%vpcomp;)+, (%vpchead;), (%vpcomp;)+ ) |( (%vpcomp;)+, (%vpchead;) )

) >

72


Co-reference Relations

• Co-reference is represented via variables• Each variable is declared as a value of an ID attribute in the

CoIndex element• The variable is used as a value of an IDREF attribute• Two elements are co-referred by pointing to the same

variable• Each co-referred relation has a type

73


Additional Levels

Except for the lexical and phrasal level, we also represent:• Multi Lexical level:

– for the result of lexical rules, and– for idiosyncratic expressions

• Discourse level:– for representation of co-reference relations to other sentences

within the discourse: InDiscourse and OutDiscourse variables

74


Bulgarian Grammar: Introducing the Background (1)

• Towards the design and the application of:– wide-coverage grammars, based on– deep linguistic knowledge

• In HPSG there already exist quite extensive implemented formal grammars– English (Flickinger 2000), German (Müller and Kasper 2000),

Japanese (Siegel 2000, Siegel and Bender 2002)– Use of Minimal Recursion Semantics (Copestake et. al 2005).

75


Introducing the Background (2)

• The international initiative LinGO Grammar Matrix (Bender et. al 2010; Bender et. al 2002)

• Customized grammars on: Norwegian, French, Korean, Italian, Modern Greek, Spanish, Portuguese (http://www.delph-in.net/index.php?page=3)

• Open source software system, which supports the grammar and lexicon development – LKB (Linguistic Knowledge Builder)

(http://wiki.delph-in.net/moin/LkbTop)

76


Grammar Matrix Architecture (1)

• Intended as a typological core for initiating the grammar writing on a specific language (Bender et. al 2002)

• Provides a customization web interface (Bender et. al 2010)• Aims at:

– common basis for comparing various language grammars to speed up the process of the grammar development

77


Grammar Matrix Architecture (2)

• Supplies the skeleton of the grammar – the type hierarchy with basic types and features

• Based on the experience with several languages (predominantly English, German and Japanese)

• Aims at semantic modeling of a language:– referential entities and events– semantic relations– semantic encoding and contribution of the linguistic phenomena

78


Example

individual := semarg & [ SORT semsort ].event-or-ref-index := individual.

ref-ind := index & event-or-ref-index & [ PNG png ].

79


Challenges (1)

• Languages like Bulgarian grammaticalize a lot of linguistic phenomena. Thus, the most common level of description would be the morphosyntactic level rather than the semantic one

• Grammar Matrix is implemented in accordance with some version of the HPSG theory – thus it implies certain decisions with respect to the possible analyses

80


Challenges (2)

• The Grammar Matrix predefines some phenomena too strictly, for other – it gives possibilities for generalizations

• Depending on the preference of the modelled linguistic phenomena, the grammar developers might have to extend and/or change the core grammar

• How much information to be encoded within the grammar, and which steps to be manipulated outside the grammar

81


Localization for Bulgarian

• The Multilingual Testset

• The Language Specific Phenomena

82


The Multilingual Testset

• 100 sentences, which in the Bulgarian translated set became 178• The translated set incorporated also a bunch of language

specific phenomenaResult:• 193 positive sentences• 20 negative sentencesComparable to the testset for Portuguese in the first phase of the

grammar development

83


81

1. Aбрамс знаеше, че вали (дъжд).Abrams knew that rains-it (rain).2. Aбрамс знаеше, че е валяло (дъжд).Abrams knew that is-rained-it (rain).

Abrams knew that it rained.

91Aбрамс възнамеряваше/се канеше да лае/да излае.Abrams(masc) was-intending/reflexive was-about to bark/to bark one or more times.

Abrams intended to bark.

101Aбрамс искаше Браун да лае/да излае.Abrams wanted Browne to bark/to bark one or more times.

Abrams intended Browne to bark.

111Всяка котка лаеше/излая.Every(fem) cat(fem) was-barking/barked one or more times. Every cat barked.

121Всяка котка преследваше някакво куче.Every(fem) cat(fem) was-chasing some(neut) dog(neut).

Every cat chased some dog.

84


Core Phenomena

• Complementation • Modification • Coordination • Agreement • Control • Quantification • Negation

• Illocutionary force• Passivization • Nominalization • Relative clauses • Light verb constructions • Other

85


Type Hierarchy

• The types in the initial grammar are 297 due to the small lexicon

• Examples:n_-_cf_le := basic-common-noun-intr & [ SYNSEM.LOCAL.AGR.PNG.GENDER feminine].nom-pers-pro-noun-le := reg-pers-pro-noun & [SYNSEM.LOCAL.CAT.HEAD.CASE nom ].

86


Some Specificities

• Bulgarian is a pro-drop language• Bulgarian verbs encode aspect lexically• More Bulgarian verb synonyms have been provided to the

English oneAbrams handed the cigarette to Browne дам (give), подам (pass), връча (deliver), предам (hand in)

• Bulgarian has clitic counterparts to the complements as well as a clitic reduplication mechanism

87


The Language Specific Phenomena: Semantics vs. Morphosyntax• The information has to be often split between the semantic

phenomenon and its realization

• For example, the adjectives, participles, numerals happen to have morphologically definite forms, while the definiteness marker is not a semantic property of these categories.

88


Definiteness

The event selects for a semantically definite[SYNSEM.LOCAL.HOOK.INDEX.DEF+]

But morphologically indefinite noun [SYNSEM.LOCAL.AGR.DEF-]Example:

старото куче ‘old-the dog’

89


Semantic Information

Grammar Matrix provides several possibilities to get the semantic information:• For tense and mood the aggregated one is chosen AGR.E.TENSE• While for aspect – the separated encodings

(HEAD.TAM.ASPECT) – (Tense, Aspect, Mood)• The aggregated way is a better choice for unified

syntactic-semantic analysis • While the separated representation leaves out an opportunity for

different manipulation of syntactic and semantic contribution

90


Generalized Types

Very often, in Bulgarian the generalization cannot be kept at higher levels

because of

The variety in the morphosyntactic behaviour types within the Bulgarian constructions

91


Copula Constructions

Adjectives, adverbs and prepositions have an event index, they cannot share the same generalized type, because:• Adjectives structure-share their PNG (person, number and

gender) characteristics with the copula’s XARG – the subject• The adverbs have to be restricted to intersective modifiers when

taken as complementsAll these heads raise their semantic index to the copula, which

is semantically vacuous itself

92


Noun Phrases• The nouns, however, have a referential index• No index is raised from the noun complement up to the copula• 8 lexical types are introduced:

• Two for present and past copula forms (*Am-I there; OK: Was-I there). • Each of the two then is divided into four subtypes depending on the

complement – present copula –noun; present copula – adjective

93


Relatively Free Word Order

Most of the rules include all the possible orders in spite of the canonical readings

Example (rules for):head-modifier and modifier-head clitic-head and head-clitic

BulTreeBank is used as a discriminative tool, because it comprises the canonical and most preferred analysis per sentence

94


Bulgarian Argument-Related Clitics

• Clitics are viewed as lexical projections of the head (i.e. operated by special rules)

• While the regular forms are treated as head arguments (complements) (i.e. operated by head-complement principles)

• The clitic does not contribute its separate semantics, because it is not a full-fledged complement

95


The Rich Morphology

Two ways of morphology incorporation were possible:

• Re-design of the whole systematic and unsystematic morphology within the grammar, which would be a linguistically sound, but time-consuming step

• Opportunistic - the inflection classes of the morphological dictionary for Bulgarian (Popov et. al 2003) have been transferred into the grammar

96


Morphological Rules

• The rich morphology requires 2600 rulesThus:

The morphological work has been suppressed in the name of syntactic and semantic modeling

But:A large lexicon could not operate without the complete set

of the morphosyntactic types and rules

97


Encoding of Morphological Rulesima_v1 := v_there-is_le & [ STEM < "има" >, SYNSEM.LKEYS.KEYREL.PRED "има_v_1_rel" ].

ima_v1 := v_there-is_le & [ STEM < "има" >, SYNSEM [ LKEYS.KEYREL.PRED "има_v_1_rel",

LOCAL.CAT.HEAD.MCLASS [ FIN-PRESENT finite-present 101, FIN-AORIST finite-aorist-080, FIN-IMPERF finite-imperf-025, PART-IMPERF participle-imperf-024, PART-AORIST participle-aorist-095 ] ] ].

98

30th European Summer School in Logic, Language and Information (6 August – 17 August 2018) 99

it rained


buy him

house


Evaluation

• Within the system [tdsb] (Oepen 2001)• The average of distinct analyses is 3.73• The ambiguity of analyses is mainly due to the following

factors: 1. Morphological homonymy of the word forms2. More than one possible word order3. More than one possible attachment 4. Competing rules in the grammar


BulTreeBank for the Selection of the Correct ParsesTask: To use the analyses in BulTreeBank for extracting the necessary discriminating properties for disambiguation

Motivation: from 654 analyses in BURGER, only 81 analyses are unique. 348 analyses have been rejected, which is more than 50 %. Hence, a disambiguation mechanism is needed.


Main Sources of Ambiguity

• Morphological ambiguity• Various places of attachment• Neutral vs. focused ordering of constituents• Proliferation of several competing rules for the same item


Ambiguities (1)

• Morphological ambiguity:1. Абрамс даде цигара на Браун. Abrahms gives/gave cigarette to Brown. Abrahms is giving/gave a cigarette to Brown.

• Various places of attachment:2. Котката е в градината. Cat-the is in garden-the The cat is in the garden.

104


Ambiguities (2)

• Neutral vs. focused ordering of constituents3. Онова куче преследваше Браун. That dog was-chasing Brown. That dog was chasing Brown.

• Proliferation of several competing rules for the same item 4 (1). Абрамс даде цигара на Браун.

Abrahms gives/gave cigarette to Brown. Abrahms is giving/gave a cigarette to Brown.

105


Implementation

A common target format to represent the important knowledge from both treebanks:• The analyses from both treebanks are transformed into a

new format;• The knowledge within the new representations is unified on

the basis of correspondences rules;• The parse selection is done on the basis of comparing both

unified representations.

106


New Format

Sentencew1 w2 w3 … wn

Lexical elementswk:pos list-of-categories

Head-dependent pair<wi:posi, wj:posj> list-of-categories

107


Example: Output Representation

• Lexical elementsкога (when):1 (ADV)лаеше (barked):2 (FINITE-IMPERF-THIRD-SG00476-ORULE)кучето (the dog):3 (THIRD_SG_NEUTER_NOUN_IRULE, DEF THIRD_SG_NEUTER_NOUN_ORULE)

• Head-dependent pair<кога (when):1 лаеше (barked):2> (MOD-INT-OTHER-PHRASE)<лаеше (barked):2 кучето (the dog):3> (HEAD-SUBJ)

108


Results

• 86 % of the correct analyses produced by BURGER were successfully selected by the discrimination properties extracted from BulTreebank

• Therefore, our idea of using BulTreebank as a discriminator of the analyses is justified

109


Problematic Cases

• Coordination (BulTreeBank accepts only one interpretation among more possible ones)

• Complementation in NP (in BulTreeBank some dependants are viewed as modifiers, while in BURGER – as complements)

110


Conclusions

The presented annotation scheme is designed to support the building of the core set of sentences

The annotation scheme combines pure linguistic features with metafeatures like recursivity, heaviness, specificity

The scheme is robust and annotator-friendlyThe annotated data could be used for selection of the correct

analyses in creation of a grammar-based version of the treebank

111


Some References

Kiril Simov, Petya Osenova, Alexander Simov, Milen Kouylekov. Design and Implementation of the Bulgarian HPSG-based Treebank. Special Issue on Treebanks and Linguistic Theories. Research on Language & Computation. Springer Science+Business Media B.V. Volume 2, Number 4.

Petya Osenova. Localizing a Core HPSG-based Grammar for Bulgarian. In: Hanna Hedeland, Thomas Schmidt, Kai Worner (eds.) Multilingual Resources and Multilingual Applications, Proceedings of GSCL 2011, ISSN 0176-599X, Hamburg, pp. 175–180.

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