detection of relations in textual documents manuela kunze, dietmar rösner university of magdeburg c...

Detection of Relations in Textual Documents

Manuela Kunze,

Dietmar Rösner

University of Magdeburg Knowledge Based Systems and Document Processing

Kunze, Rösner: Detection of Relations in Textual Documents 2

Introduction

http://en.wikipedia.org/wiki/Unsupervised_learning


Introduction

• to extract information from text, you can use techniques like simple pattern matching etc.

• additional knowledge is required:• 'Thursday': a day of a week• meaning of

• (implicit) `open' vs. `close'• `Pay-what-you-wish'

• text understanding / techniques of NLP • `Exhibition of over 30 color photographs and stories of life in

China's Yunnan Province …'


Introduction

ontologies contain information about:

• definition/description of concepts and

• description of instances

• kind of relation (name, type),– definition of domain and range values,

– characteristic of the relation: cardinality, transitivity, ...,


Natural Language Processing

• NLP techniques: – case frame analysis– exploiting syntactic structures– corpus-based IE for an initial ontology

• corpus:– autopsy protocols (400 protocols)– different document parts:

• findings• histological findings• background• discussion• …

– short linguistic structures – typical attribute-value structures


Overview

Case Frame

Analysis of Specific Syntactic Structures

Discussion/Conclusion


Case Frames

• resources:– results from syntactic parser

<NP TYPE="COMPLEX" RULE="NPC3" GEN="MAS" NUM="SG" CAS="NOM"> <NP TYPE="FULL" RULE="NP1" CAS="NOM" NUM="SG" GEN="MAS"> <N>Flachschnitt</N> </NP> <PP RULE="PP1" CAS="AKK"> <PRP CAS="AKK">in</PRP> <NP TYPE="FULL" RULE="NP2" CAS="AKK" NUM="SG" GEN="NTR"> <DETD>das</DETD> <N>Zungengewebe</N> </NP> </PP> </NP>

– results from semantic tagger– description of case frames


Case Frames

• (corpus-based) definition of roles for a concept– `Flachschnitt' (flat cut)

• `location'– sem. category: `tissue'– PP, case of NP: accusative, preposition: `in'

– `Herausschleudern' (skidding)• `patient'

– sem. category: `body-hum'– NP; case of NP: genitive

• `location' – sem. category: `vehicle' – PP, case of NP: dative, preposition: `aus'


Case Frames…<CONCEPT TYPE="medicalOperation">

<WORD>Flachschnitt</WORD> <DESC>medizinischer Schnitt</DESC> <SLOTS> <RELATION TYPE="LOCATION"> <ASSIGN_TO>TISSUE</ASSIGN_TO> <FORM>P(akk, fak, in)</FORM> <CONTENT>in das Zungengewebe</CONTENT> </RELATION> </SLOTS> </CONCEPT>

<CONCEPT TYPE="traffic-event"> <WORD>Herausschleudern</WORD> <DESC>event</DESC> <SLOTS> <RELATION TYPE="PATIENT"> <ASSIGN_TO>BODY-HUM</ASSIGN_TO> <FORM>N(gen, fak)</FORM> <CONTENT>des Koerpers</CONTENT> </RELATION> <RELATION TYPE="LOCATION"> <ASSIGN_TO>VEHICLE</ASSIGN_TO> <FORM>P(dat, fak, aus)</FORM> <CONTENT></CONTENT> </RELATION> </SLOTS> </CONCEPT>

…


Case Frames

• coverage of phrases like `fracture of elbow joint'?

• abstraction– `fracture' (sem. category: `trauma')

• role `patient': sem. category: `bone'

– `bruise' (sem. category: `trauma')• role `patient': sem. category: `organ'

– `hematoma' (sem. category: `trauma')• role `patient': sem. category: `tissue'

• concept x (sem. category: `trauma')– role `patient': sem. category: `body-part'


Case Frames

• results:– relations are defined by the case frame

• name/type of relation• domain, range

– corpus-based abstractions:• redefinition of semantic restriction

– use the least general hypernym as semantic restriction

• not yet extracted:– information about the characteristic of a relation


Overview

Case Frame





• from general to specific information• resources:

– results from syntactic parser– results from semantic tagger– description of interpretation of syntactic structures

• Which word class can be interpreted as concept/instance?

• Which word class describes a relation?– adjective in a NP: describes the noun in the NP relation `prop‘– negations: negate concepts, verbs, or properties of a concept– particle: modification of adjectives



CLMed N ADJ

prop(N, ADJ)

N interpreted as concept

ADJ interpreted as concept

results:

prop_catadj(N,ADJ)



`liver tissue bloodless‘

Steps:

bloodless*blood

concentrationbloodless

liver_tissue* tissueliver tissue

• nouns and adjectives are interpreted as concept/instance

• adjectives describe a relation• in general: 'prop'

prop_blood-concentrationprop_blood-concentration

conceptinstancerelation


Analysis of Specific Syntactic Structures`liver tissue bloodless‘

…

<owl:Class rdf:ID="lebergewebe">

<rdfs:subClassOf><owl:Class rdf:ID="tissue"/></rdfs:subClassOf></owl:Class>

<owl:Class rdf:ID="blood-concentration"/>

<owl:Class rdf:ID="blutleer">

<rdfs:subClassOf rdf:resource="#blood-concentration"/></owl:Class>

<owl:ObjectProperty rdf:ID="prop_blood-concentration">

<rdfs:domain rdf:resource="#tissue"/><rdfs:range rdf:resource="#blood-concentration"/></owl:ObjectProperty>

<lebergewebe rdf:ID="Lebergewebe_6">

<prop_blood-concentration><blutleer rdf:ID="blutleer_7"/></prop_blood-concentration></lebergewebe> …


Analysis of Specific Syntactic Structures"kaum wahrnehmbare Unterblutungen"(Engl. "hardly detectable hematomas")

results of syntactic parser:<NP TYPE="FULL" RULE="NP4" CAS="_" NUM="PL" GEN="FEM">

<ADJP RULE="ADJP1">

<ADV>kaum</ADV>

<ADJ>wahrnehmbare</ADJ>

</ADJP>

<N>Unterblutungen</N>

</NP>

results of semantic tagger:– `kaum': weak-graduation– `wahrnehmbar': unknown token– `Unterblutung': trauma

resources for interpretation:• N: concept/instance• ADJ:

• concept/instance• rel: prop

• ADV:• concept/instance• rel: mod

adverb specifies adjective

adjective specifies noun



`hardly detectable hematomas‘ Steps:

detectable* unspecified

hematoma* traumahematoma

• nouns, adjectives and adverbs are interpreted as concept/instance

• adjectives and adverbs describe relations

prop_unspecifiedprop_unspecified


hardly* hardly weak-graduation

mod_weak-graduationmod_weak-graduation


Analysis of Specific Syntactic Structures`hardly detectable hematomas‘

<owl:Class rdf:ID="unterblutung"><rdfs:subClassOf rdf:resource="#trauma"/></owl:Class>

<owl:Class rdf:ID="trauma"/>

<owl:Class rdf:ID="wahrnehmbar">

<rdfs:subClassOf rdf:resource="#unspecified"/></owl:Class>

<owl:Class rdf:ID="unspecified"/>

<owl:Class rdf:ID="kaum">

<rdfs:subClassOf rdf:resource="#weak-graduation"/></owl:Class>

<owl:Class rdf:ID="weak-graduation"/>


Analysis of Specific Syntactic Structures`hardly detectable hematomas‘

<owl:ObjectProperty rdf:ID="mod_weak-graduation">

<rdfs:domain rdf:resource="#unspecified"/>

<rdfs:range rdf:resource="#weak-graduation"/></owl:ObjectProperty>

<owl:ObjectProperty rdf:ID="prop_unspecified">

<rdfs:domain rdf:resource="#trauma"/>

<rdfs:range rdf:resource="#unspecified"/></owl:ObjectProperty>

<unterblutung rdf:ID="Unterblutungen_5">

<prop_unspecified rdf:resource="#wahrnehmbare_4"/></unterblutung>

<wahrnehmbar rdf:ID="wahrnehmbare_4">

<mod_weak-graduation rdf:resource="#kaum_3"/></wahrnehmbar>

<kaum rdf:ID="kaum_3"></kaum>




Protégé Plugin for Visualization: Ontoviz

Phrases like: • NP NP NP• NP N Adj Conj Adj• NP N conj N Adj• …



• results– definition of concepts/instances– corpus-based definition/concretion of relations:

• prop prop_catADJ

• information about domain, relation

• not extracted:– information about the characteristic of a relation


Overview

Case Frame




Conclusion

• NLP techniques for extraction of information– analyse syntactic structures – information about semantic categories– result: corpus-based description of an initial ontology

• case frame analysis– relations are described in the case frame– disadvantage: creation of case frames– advantage: a definition of the relation

• analysis specific syntactic structures– a general interpretation of tokens and the syntactic structures– redefined by results from the semantic tagger– disadvantage: in some case, only the general relation definition is

delivered– advantage: less effort to describe the resources


Conclusion

• no information about the characteristic of a relation (cardinality, …)

• solutions– analyse occurrences in the corpus

• corpus-based assumption about cardinality

– integration of additional knowledge• initial domain specific ontology


Key Aspects for IE

• ‘conceptual’ preprocessing steps: Names of concepts occur in different linguistic structures; compound vs. complex noun phrase (like ‘liver tissue’ and ’tissue of liver’)

– handle only one canonical linguistic structure as a representative for all paraphrases

• treatment of generalisation within local contexts – The token ‘liver’ may occur in the first sentence of a paragraph. In the next sentences

of the paragraph, only the hypernym ‘organ’ is used.

• concept or instance: which term in a linguistic structure has to be interpreted as a concept and which as an instance of a concept resp.

• definition of the scope for a concept: – a paragraph starts with a description of an organ (e.g. organ ‘liver’ in: ‘The liver

shows ... . Bloodrichness of the tissue.’ ), after this follows a description of parts of the organ (e.g., ‘Gewebe’). In such cases, additional knowledge about the domain has to be employed (for example, about meronyms or holonyms)

– tissue part-of liver vs tissue part-of concept X

detection of relations in textual documents manuela kunze, dietmar rösner university of magdeburg c...

Documents

detection of relations

relation slide

aus slide

learning slide

document processing

bodyhum np case of np

trauma role

chinas yunnan province