Corpus based Creation and Extension of Domain-Specific Resources Manuela Kunze,

Dietmar RösnerUniversity of Magdeburg

Manuela Kunze 2

Overview

Background: Corpus Characteristics

Experiment 1: Context-related Derivation of Concepts

Experiment 2: Clustering of Values

Manuela Kunze 3

Corpus: Forensic Autopsy Protocols different document parts:

findings histological findings background discussion …

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Autopsy Protocols: Findings

short linguistic structures typical attribute-value structures

expressed by noun phrases:

Unterblutung des Gewebes/Bleeding of tissue. Oberlippenbart/Upper lip beard.

noun phrases + verb/adjective/noun phrase Mund geschlossen./Mouth closed. Nebennieren ohne Besonderheiten./Adrenal glands

without anomalies.

Useable for the extension of the resources in combination with GermaNet?

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Corpus

400 Protocols parsed with a context free grammar (ca.

40 rules)

focus of the analyses complex noun phrases

derivation of concepts

attribute-value structuresclustering of values

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Overview

Corpus Characteristics

Experiment 1: Context-related Derivation of Concepts

Experiment 2: Clustering of Values

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Approach

analysis of high-frequency complex noun phrases example: Bruch des/der … (fracture of …) occurrence 749 types: 93

known (31): Rippe/rib (254), Brustbein/sternum (65), Wirbelsäule/spine

(58), Schambein/pubic bone (30), Schulterblatt/omoplate (23), …

unknown (62): Schädeldach/calvarium (43), Oberschenkelknochen/femur

(37), Schädelbasis/base of the skull (34), Schlüsselbein/clavicle (33), Brustwirbelsäule/thoracic spine (28), Halswirbelsäule/cervical spine (26), …

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Idea: Analysis of Complex Noun Phrases

fracture of <known>

keyword of complement

fracture of <unknown>

in corpus:

class of <known>

deduce: class of <unknown> == class of <known>

in GermaNet:

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Approach

top level category : T

remove senses which are not assigned with the preferred top level

category

collect all (GermaNet) senses

determine the most frequent top level

category

known complements types of a keyword

collect all semantic classes from the hypernym graph

for

each

sen

se known (31): Rippe/rib (254),

Brustbein/sternum (65), Wirbelsäule/spine (58), Schambein/pubic bone (30), Schulterblatt/omoplate (23), …

high-frequency top level categories (as percentage)

3

16,5

75

5,5

noun.body

noun.artifact

noun.quantity

noun.food

…<nomen.Koerper>Finger <nomen.Koerper>=> Gliedmaße, Extremität

<nomen.Artefakt>Finger <nomen.Artefakt>=> Computerprogramm, Programm

<nomen.Koerper>Rippe <nomen.Koerper>=> Knochen, Gebein…

top level category: noun.body

36 senses 27 senses

22 different semantic classes

36 senses

…<nomen.Koerper>Rippe, <nomen.Koerper>=> Knochen, Gebein, <nomen.Koerper>=> Hornsubstanz, <nomen.Koerper>=> Körpersubstanz, <nomen.Substanz>=> Stoff1, Substanz, Materie, <nomen.Tops>=> Objekt, <nomen.Koerper>=> Hornsubstanz, <nomen.Koerper>=> Körpersubstanz,<nomen.Substanz>=> Stoff1, Substanz, Materie, <nomen.Tops>=> Objekt, …

31 complement types

…<nomen.Koerper>Finger <nomen.Koerper>=> Gliedmaße, Extremität

<nomen.Artefakt>Finger <nomen.Artefakt>=> Computerprogramm, Programm

<nomen.Koerper>Rippe <nomen.Koerper>=> Knochen, Gebein

…

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Approach

collect all semantic classes from the hypernym graph

for each semantic class sc: • determine the level in the hypernym tree (fsc)• count occurences (nsc)

most specific semantic class: Knochen

22 different semantic classes

select the maximum of(fsc * nsc)/N

N: number of all semantic classes

…<nomen.Koerper>Rippe, <nomen.Koerper>=> Knochen, Gebein, <nomen.Koerper>=> Hornsubstanz, <nomen.Koerper>=> Körpersubstanz, <nomen.Substanz>=> Stoff1, Substanz, Materie, <nomen.Tops>=> Objekt, <nomen.Koerper>=> Hornsubstanz, <nomen.Koerper>=> Körpersubstanz,<nomen.Substanz>=> Stoff1, Substanz, Materie, <nomen.Tops>=> Objekt, …

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Results

85 % correct assignments (types) 94 % correct assignments (tokens)

erroneous cases: correct assignments to wrong complements wrong assignments to correct complements

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Results: Erroneous Cases

correct assignments to wrong complements: misspelling of tokens: „Oberschenkelknorren“ erroneous fragments of the treatment of German‘s

truncations: „Bruch des Ober- und Unterarmes“ erroneous syntactic analysis of the second NP: „Bruch der

Wandung der …“

wrong assignments to correct complements: (complex) systems of bones, cartilages, connective

tissues: „elbow joint“

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Overview

Corpus Characteristics

Experiment 1: Context-related Derivation of Concepts

Experiment 2: Clustering of Values

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Clustering of Values conceptual analysis of linguistic structures

Mund geschlossen/Mouth closed. Rachenschleimhaut duesterrot. /Mucosa of fauces dark red. Beckengeruest festgefuegt und unversehrt. /Pelvis closely joined and entire. Herzohren frei, ovales Vorhoffenster geschlossen./Auricles of heart clear, oval atrium

closed. Brustbein, Rippen und Wirbelsaeule intakt./Sternum, ribs and spine intact. Brustkorb sehr schmal und leicht eindrueckbar./Thorax very narrow and easy to incise. Nebennieren ohne Besonderheiten./Adrenal glands without anomalies. …

1908 concepts Mund/mouth Rachenschleimhaut/mucosa of fauces Beckengeruest/pelvis Herzohren, Vorhoffenster/auricles of

heart, atrium Brustbein, Rippen,

Wirbelsaeule/sternum, ribs, spine Brustkorb/thorax Nebennieren/adrenal glands

2098 different (linguistic) values geschlossen/closed duesterrot /dark red festgefuegt, unversehrt /closely joined,

entire frei, geschlossen/clear, closed intakt/intact sehr schmal, leicht eindrueckbar/very

narrow, easy to incise ohne Besonderheiten/ without anomalies

Have similar concepts same attributes?

What are the values for an attribute?

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Relations Between Values

Do the values describe different attributes? color, shape etc.

if not, are the values paraphrases/synonyms? antonyms? values of an ‚open‘ range?

Which lexical or conceptual relations exist between the values, e.g.

synonyms, antonyms etc.?

clustering of values

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Examples

Mund/mouth:

deutlich geoeffnet

fischmaulartig geoeffnet schlotartig geoeffnetruesselartig geoeffnetfroschmaulartig geoeffnetovalaer geoeffnetgeoeffnetspaltfoermig geoeffnetgeschlossen

different kinds of 'opened' vs. closed

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Examples

Milzgewebe/spleen tissue:nicht sehr blutreichfest deutlich gelockertstark gelockertrelativ gelockert verhaertet gelockertleicht gelockert blutreich sehr blutarmfaeulnisbedingt gelockertetwas faeulnisbedingt aufgelockert sehr blutreich

concentration of blood

consistency,form of tissue

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Examples

Wirbelsaeule/spine:ebenfalls unversehrt ebenfalls intakt intaktunversehrt ohne Besonderheitenohne Verletzungen

same findings

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Approach

comparison of values of a concept 33670 comparisons

comparison in several steps1. character-based: via bigrams2. lexical-conceptual relations: available

information in Germanet

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Approach

values of a concept

removing negations

removing modificators

'corrected' values

lexical/conceptualrelations in GermaNet?

compound?

bigrams of values

particles: sehr, sonst, ebenfalls

adjectives with suffixes: ‚-artig‘, ‚-lich‘, ‚-ig‘

example: 'sonst unaufällig' 'unauffällig'

negations: 'kein', 'nicht', …

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Results: Character-based Analysis similar values with modifications (particles) and negations

selbst unauffaellig

sonst unauffaelligunauffaellig

glaenzend

nicht glaenzend

geoeffnet

leicht geoeffnet

rundlich geoeffnet

spaltfoermig geoeffnet

spaltweit geoeffnetfroschmaulartig geoeffnet … geoeffnet

sehr muskelkraeftig

nicht sehr muskelstark

muskelkraeftig

nicht sehr muskelkraeftignicht muskelkraeftig

blutreichnicht-sehr-blutreich

sehr-blutreich

blutarmrelativ-blutarm

muskelschwachsehr-muskelschwach

geschlossenspaltfoermig-geschlossen

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Integration of GermaNet

search for relations between two tokens parts of tokens

queries about: coordinate terms synonyms, hypernyms, hyponyms antonyms

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Results with GermaNet sehr muskelkraeftig/very strong muscle vs. sehr muskelschwach/very

weak muscle bigrams: 0.5882, 0.4167 antonym: kraeftig vs. schwach

blutarm/bloodless vs. blutreich/bloodrich bigrams: 0.4286 GermaNet: antonym: arm vs. reich

feucht/wet vs. sehr trocken/very dry bigrams: 0.0000 GermaNet: coordinate terms, antonym

sehr gross/very great vs. sehr weit/very broad bigrams: 0.4706 GermaNet: hypernym

frei/free vs. größtenteils vorhanden/mostly existent bigrams: 0.0833 GermaNet: coordinate terms

keine Schwellung/no swelling vs. keine Verletzung/no trauma bigrams: 0.42, 0.4 GermaNet: hypernym

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Results: Character-based + GermaNet

selbst unauffaellig

sonst unauffaelligunauffaellig

glaenzend

nicht glaenzend

blutreichnicht-sehr-blutreich

sehr-blutreich

blutarmrelativ-blutarm

sehr muskelkraeftignicht sehr muskelstark

muskelkraeftig

nicht sehr muskelkraeftignicht muskelkraeftig

muskelschwachsehr-muskelschwach

geoeffnet

leicht geoeffnet

rundlich geoeffnet

spaltfoermig geoeffnet

spaltweit geoeffnetfroschmaulartig geoeffnet … geoeffnet

geschlossenspaltfoermig-geschlossen

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Problem: Paraphrases

Wirbelsaeule/spine:intaktunversehrt ohne Besonderheitenohne Verletzungen

same findings

future work

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Idea: Detection of Paraphases/Synonyms document information + corpus information

to analyse the value sets of a document

compare the value sets of a concept described in different documents values, which are synonyms or antonyms don‘t occur in a

document Example:

Spine closely joined and entire. closely joined, entire: different attributes

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Idea: Detection of Paraphases/Synonyms collect all values for a concept: candidates

• entire• closely

jointed

• entire• closely

jointed

candidates: intact == broken == entire/closely jointed == entire ?

AP#1 Ap#nAP#2 AP#3 …

…• broken • intact • intact

AP#4 AP#5

• entire

values for the concept 'spine':

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Idea: Detection of Paraphases/Synonyms

0

20

40

60

80

100

120

140

160

180

intact bleedings closely joined entire without anomalies without bleedings withoutpathological

findings

removing of candidates:

only one paraphrase

bleedings or without bleedings antonyms

closely joined vs. entire occur in the same document (for a concept)

prefer: entire (number of occurrences)

assumption: closely joined is an 'additional' attribute

selection of candidates (restrictions):

only frequent values

similar number of occurrences?

verification of results:

to obtain value sets of other concepts

which have similar values

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Problems: Detection of Paraphrases a value can be expressed by more than one value

'value 1' == 'value 2' + 'value 3'

result (set of paraphrases for a value) can contain antonyms

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Detection of Paraphases/Synonyms solutions?

integration of other resources: UMLS extension of GermaNet

1 sense of unversehrt

Sense 1<adj.Koerper>unverletzt, unversehrt <adj.Koerper>=> heil <adj.Koerper>=> gesund <adj.Koerper>=> ?krankheitsspezifisch <adj.Koerper>=> ?körperzustandsspezifisch <adj.Koerper>=> ?körperspezifisch

1 sense of intakt

Sense 1<adj.Relation>intakt, ganz1, funktionstüchtig, funktionsfähig <adj.Relation>=> ?funktionalitätsspezifisch <adj.Relation>=> ?relationsspezifisch

same meaning?

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Conclusion

experiments about corpus based semiautomatic extension of GermaNet

analysis of complex noun phrases detection and transfer of GermaNet classes

clustering of values bigrams using GermaNet information