Ирина Гуревич "Язык программирования – это не остров:...

112.09.2014 | Technische Universität Darmstadt | Iryna Gurevych

Programming language is not an island: Word Sense Alignment of Lexical-Semantic Resources

Iryna GurevychJoint work with: Judith Eckle-Kohler, Kostadin Cholakov, Silvana Hartmann, Michael Matuschek, Christian M. Meyerhttp://www.ukp.tu-darmstadt.de/data/uby

UBY

http://www.ukp.tu-darmstadt.de/data/uby

2

Motivation

Similarity-based Word Sense Alignment

Graph-based Word Sense Alignment


Outline

Joint Modeling of Features

Putting the Pieces Together: UBY

Applications of Linked Lexical Resources


Text Analysis Needs Lexical-Semantic Knowledge

Lexical resourceNLP application

Which lexical resource to choose?


Resources are Largely Different

Different coverage of words/word sensesDifferent types of informationEncyclopedic vs. linguistic knowledgeSyntactic vs. semantic knowledge…

Resource integration can significantly influence the performance of your system! – Instead of choosing only one (best performing):

Why not combine multiple resources and benefit from all their knowledge?


Overlap of Lexical Entries

25,541

7,885 1,729

56,240

28,650

Roget’s Thesaurus(62,797)

Wiktionary(364,663)

WordNet(149,502)

163,027 67,868

Common vocabulary is rather small (28,650).

Each resource contains a lot of “unique” words.


Overlap of Lexical Entries

slang

dialect

naturalsciences

computerscience

neologisms

humanities

socialsciences

biologicaltaxonomy

named entities

surprisinglysmall

overlap

math

7

1. To sing: To produce musical or harmonious sounds with one’s voice.

2. To sing: To express audibly by means of a harmonious vocalization. 3. To sing: To confess under interrogation.

1. singen: Mit der Stimme harmonische Töne erzeugen.

1. To sing: Produce tones with the voice

2. To sing: divulge confidential information or secrets

1. To sing: To produce harmonious sounds with one's voice.


Word Sense Alignment


Prior Work on Linked Lexical Resources (LLR)

Meaning Multilingual Central Repository, Atserias et al. (2004)Yago, Suchanek et al. (2007)SemLink (Palmer, 2009)Universal Wordnet (UWN), Gerard de Melo and Gerhard Weikum

(2009)eXtended WordFrameNet, Laparra and Rigau (2010)BabelNet, Navigli and Ponzetto (2010)NULEX, McFate and Forbus (2011)UBY, Gurevych et al. (2012)… many more, e.g. on the Semantic Web


Potential of Linked Lexical Resources

Increased coverage and the enriched sense representation

Linking FrameNet, VerbNet, and WordNet for semantic parsing (Shi and Mihalcea, 2005)

Linking VerbNet, FrameNet and PropBank for semantic role labeling (Palmer, 2009)

Linking WordNet and Wikipedia for word sense disambiguation (Navigli and Ponzetto, 2010)

Linking WordNet and Wiktionary for measuring verb similarity (Meyer and Gurevych, 2012)

Linking OmegaWiki and Wiktionary for mining translations (McCrae and Cimiano, 2013)


The Challenge: Heterogeneity of Resources

Different coverage:missing entities in one of the resources

Different granularity:entities are defined at different levels

Different perspectives:entities are defined for a different purpose

vs.

vs.

vs.

(Euzenat/Shvaiko, 2007)


Lemma Alignment

Wiktionary

WordNet

Content integration at the lemma level is easy, but…



Wiktionary

WordNet

…integration at the sense level is hard!

Content integration at the lemma level is easy, but…



plant in Wiktionary (botany) An organism of the kingdom

Plantae […]

(proscribed as biologically inaccurate) Any creature that grows on soil or similar surfaces, including plants and fungi.

A factory or other industrial or institutional building or facility.

(snooker) A play in which the cue ball knocks one (usually red) ball onto another […]

plant in WordNet buildings for carrying on

industrial labor

(botany) a living organism lacking the power of locomotion

an actor situated in the audience whose acting is rehearsed but seems spontaneous to the audience

?

?


The Alignment Process

Can be generalized for multiple resources „multi-alignment“:

Matching

parameters p

knowledge k

A‘A

r

r‘

resource 1

resource 2

initialalignment

(possibly empty)

outputalignment

A‘ = f(r, r‘, A, p, k)

A‘ = f(r1,…,rn, A, p, k)(Euzenat/Shvaiko, 2007)

20

Motivation




Outline





Construction of aligned lexical resources

Sense Alignment

Niemann & Gurevych, IWCS 2011

█Meyer &

Gurevych, IJCNLP

2011

█

Matuschek & Gurevych, TACL, 2013

█ █ █Matuschek

& Gurevych, COLING,

2014

█ █ █

Hartmann & Gurevych, ACL 2013

█ █

Miller & Gurevych,

LREC 2014

█ █ █

█ Graph-based alignment

█ Resource-independent alignment

█ Text similarity-based alignment

█ Exploitation of existing LR alignments to produce new ones

What Psycholinguists Know About Chemistry: Aligning Wiktionary and WordNet for Increased Domain Coverage. Christian M. Meyer and Iryna Gurevych. In: Proceedings of IJCNLP, pp. 883-892, November 2011.



Enriched sense representations

Increased coverage

23

Wikipedia article …



Aligning Wiktionary and WordNet

A two-step approach:1. Candidate extraction2. Candidate disambiguation

plant (factory)

plant (organism)

plant (person)

works (machine)

bird(animal)

works (factory) …

WordNet synsets

Wiktionary senses

{plant, works, industrial plant}{plant, works,

industrial plant}{plant, works,

industrial plant}

to fly(move)reddish

(color)

24





plant (factory)

plant (organism)

plant (person)

works (machine)

bird(animal)

works (factory) …

WordNet synsets

Wiktionary senses



industrial plant}

to fly(move)reddish

(color)


25





plant (factory)

plant (organism)

plant (person)

works (machine)

bird(animal)

works (factory) …

WordNet synsets

Wiktionary senses



industrial plant}

to fly(move)reddish

(color)

X

X

X



Bag of Words Representation

hypernyms

synset

hyponyms

hyper- & hyponyms

bag-of-words

bag-of-words

sense definition

lemma

usage examples

synonyms

Synsets are represented by synonyms, gloss, examples


Candidate Disambiguation

bag-of-words

bag-of-words

semantic relatedness

measure

s < threshold s ≥ threshold

No alignment!Align this pair of

WordNet synset and Wiktionary sense!

score sCOS: Cosine similarity

PPR: Personalized PageRank


Evaluation Dataset

Dataset creation: No previous alignments = no other evaluation datasets We created a new dataset with 2,423 sense pairs 10 human raters (students/researchers from CS, math, linguistics) Annotate each pair as “same meaning” or “different meaning”

Dataset reliability: Inter-rater agreement: AO = .93, κ = .70 Removing two biased raters: AO = .94, κ = .74

Gold standard: Majority vote of the 8 raters, additional tie breaker

Publicly available!


Evaluation Results

Method A P R F1

RAND .662 .212 .594 .313

MFS .802 .329 .508 .399

COS only .901 .598 .703 .646

PPR only .915 .684 .636 .659

COS&PPR .914 .674 .649 .661

RAND: Random baseline MFS: Baseline aligning always the first sense (≈ most frequent sense)

Our approach significantly outperforms the baseline (at 1% level) COS highest recall; PPR highest precision; COS&PPR highest F1 Significant difference of PPR, COS&PPR over COS (at 1% level) No significant difference between PPR and COS&PPR


Error Analysis

110 false negatives: “same meaning, but was not aligned”

Very different wording “good discernment” vs.“ability to notice what others might miss”

Similar senses but slightly below threshold “plants of the genus Centaurea” vs. “common weeds of the genus

Centaurea”

Pointing to another entry rather than a content-based gloss pacification: “the process of pacifying”


Error Analysis

98 false positives: “different meaning, but have been aligned”

Similar wording, but refer to different concepts “a computer that provides client stations with access to files and

printers as shared resources to a computer network” vs. “any computer attached to a network”

High relatedness, but generic- versus domain-specific vocabulary “any computer attached to a network” vs. “any organization that

provides resources and facilities for a function or event”


Increased Coverage: Parts of Speech

Wiktionary AND WordNet

Additionally inWiktionary

Additionally in WordNet

Nouns 34,464 158,085 47,651

Verbs 8,252 29,119 5,515

Adj./Adv. 14,236 60,977 7,541

Other POS 0 16,778 0

Inflected Forms 0 106,328 0

Our alignment: 56,970 sense pairs Final resource contains 488,988 word senses Substantial increase in the coverage of senses Wiktionary is not restricted to nouns/verbs/adjectives: proverbs,

idioms, collocations, particles, determiners, inflected forms, etc.


Increased Coverage: Domains

Wiktionary AND WordNet

Additionally in Wiktionary

Additionally in WordNet

Biology 4,465 4,067 12,869

Chemistry 2,561 8,260 2,268

Engineering 1,108 940 1,080

Geology 2,287 2,898 2,479

Humanities 4,949 2,700 5,060

IT 439 3,032 557

Linguistics 1,249 1,011 1,576

Math 615 2,747 483

Medicine 3,613 3,728 3,058

Military 574 426 585

Physics 1,246 2,835 1,252

Religion 733 1,154 781

Social Sciences 3,745 2,907 4,458

Sport 905 2,821 807


Enriched Sense Representation

Synonyms

Gloss

Example sentence

Subsumption hierarchy

Synset organization

…

Pronunciation

Etymology

Syntactic knowledge

Quotations

Related terms

Translations

…


Selected Conclusions

Aligned Wiktionary – WordNet is characterized by:(1) Increased coverage Different parts of speech, not only nouns e.g. humanities and social sciences from WordNet e.g. technical domains and leisure from Wiktionary

(2) Enriched sense representation Pronunciation, etymology, related terms, translations, etc.

Novel evaluation dataset annotated by 10 human raters

Better results based on the resource-structure based and hybrid techniques in later work (Matuschek & Gurevych, TACL ‘13)

48

Motivation




Outline





Construction of aligned lexical resources

Sense Alignment


█Meyer &

Gurevych, IJCNLP

2011

█



& Gurevych, COLING,

2014

█ █ █


█ █

Miller & Gurevych,

LREC 2014

█ █ █





Michael Matuschek and Iryna Gurevych: Dijkstra-WSA: A Graph-Based Approach to Word Sense Alignment, in: Transactions of the Association for Computational Linguistics (TACL), vol. 1, p. 151-164, May 2013


Similarity-Based Approaches Suffer From…

Different vocabulary employed by definitions

Example: English noun eye/discernment, e.g.,she has an eye for fresh talenthe has an artist's eye

good discernment (either visually or as if visually)

ability to notice what others might miss

low semantic relatedness score…


Solution: Use the Graph Topology

Word Senses of JavaJava1 Java2

Java3


Intuition of Graph Topology

Monosemous lexeme

programming language


Java3

programminglanguage1

53

Word Senses of Ruby



Monosemous lexeme



Java3


Ruby1



Related senses are in the same region of the graph

Word Senses of Ruby

Monosemous lexeme



Java3


Ruby1


Dijkstra-WSA

Graph-based word sense alignment approach

Key ideas:Represent lexical resources as graphsRely on trivial alignments as “reference nodes” and “bridges”Use Dijkstra’s shortest path algorithm

to find alignments

Steps:1. Graph construction2. Computing sense alignments

(Matuschek/Gurevych, 2013)


Step 1: Graph Construction

Represent each lexical resource as an undirected graph L = (V, E) with the set of nodes V representing senses or synsets the set of edges E V x V representing some kind of (semantic)

similarity between a pair of nodes

An edge connects sense S1 and sense S2 if, for example…There exists a semantic relation between S1 and S2A lexeme W2 occurs in the sense definition of S1, and

W2 is monosemous S1 and S2 share the same syntactic behavior…



Step 1: Graph Construction

Graph of resource 1

Graph of resource 2

edges representing some kind of (semantic) similarity between nodes

Java1

Java2

Java3

Java1



espresso1

espresso1


Step 2: Computing Sense Alignments

a) Create trivial alignments between the resources: Trivial = lexeme is unique/monosemous in both resources Example: programming language Precision: >0.95

b) Identify alignment candidates For example: nodes representing the same lemma

c) For all nodes still unaligned, find shortest paths to the candidate nodes in the other graph Trivial alignments serve as “bridges” between the graphs Align the node pair with the shortest path



Step 2: Computing Sense Alignments

Graph of resource 1

Graph of resource 2

Java1

Java2

Java3

Java1



espresso1

espresso1


Step 2a: Create Trivial Alignments

Graph of resource 2

Graph of resource 1

Java1

Java2

Java3

Java1



espresso1

espresso1


Step 2b: Identify Alignment Candidates

Graph of resource 2

Graph of resource 1

?

?

?

Java1

Java2

Java3

Java1



espresso1

espresso1


Step 2c: Shortest Paths to the Candidates

Graph of resource 2

Graph of resource 1

3

5

∞

Java1

Java2

Java3

Java1



espresso1

espresso1


Step 2c: Align the Nodes

Graph of resource 2

Graph of resource 1

!Java1

Java2

Java3

Java1



espresso1

espresso1


Parameter Choices

Restricting the number of alignments Stop when the first candidate is found (1:1 alignment) Keep going and align everything you can reach (1:n alignment)

Possibly with a restricted search depth

Graph construction Use semantic relations, monosemous linking, or both Get rid of relations to high frequent monosemous lexemes (e.g., there is)

Limiting to rare lexemes avoids “explosion” of edges Rare = only appearing in 1 / N of the definitions (e.g., N = 200)

Computing Sense Alignments Path length L: unbounded L yields unmanageable runtime!

Best F1 score between 5 and 8, depending on the resource pair


Hybrid Approach

Main issue of Dijkstra-WSA Low recall due to missing edges / sparse graph

Hybrid approachTry to align using the graph first Parameterized for high precisionAlign those with no match using a similarity-based approach


Evaluation Datasets

Sampled datasets: WordNet – Wikipedia (1,815 sense pairs) WordNet – Wiktionary (2,423 sense pairs) FrameNet – Wiktionary (2,789 sense pairs) WordNet – OmegaWiki (683 sense pairs) Wiktionary – OmegaWiki (586 sense pairs) Wiktionary –Wikipedia English (367 sense pairs)

Full datasets: GermaNet – Wiktionary (45,636 sense pairs) Wiktionary –Wikipedia German (31,808 sense pairs)


Datasets Display Different Properties

WordNet, OmegaWiki, Wikipedia: sense definitions and semantic relations

Wiktionary: no disambiguated semantic relations => sparse graphs GermaNet: very few sense definitions


Evaluation

Random baseline1:11st

Similarity-based (SB)

Semantic Relations (SR)Linking Monosemes (LM)

SR + LM

SR + SBLM + SB

SR + LM + SB

Human performance

Hybrid



Evaluation




SR + LM

SR + SBLM + SB

SR + LM + SB

Human performance

Hybrid


Significant improvement in recall….


Evaluation




SR + LM

SR + SBLM + SB

SR + LM + SB

Human performance

Hybrid


… and F-measure…


Evaluation

… also on all other datasets!



Dijkstra-WSA ≥ gloss similarity for densely linked LSRsÞ Generic alignment approach is validBut: low recall for sparse LSRs (English Wiktionary, OmegaWiki)

Dijkstra-WSA + similarity-based backoff outperfoms previous work on all datasets

Þ The two notions of similarity are complementaryÞ Could they be combined in a smarter way?

77

Motivation




Outline





Construction of Aligned Lexical Resources

Sense Alignment


█Meyer &

Gurevych, IJCNLP

2011

█



& Gurevych, COLING,

2014

█ █ █


█ █

Miller & Gurevych,

LREC 2014

█ █ █





Michael Matuschek and Iryna Gurevych: High Performance Word Sense Alignment by Joint Modeling of Sense Distance and Gloss Similarity, in: Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014). Dublin, Ireland.


Joint Usage of Features

Similarity- and graph-based approaches both have weaknessesDifferent formulation of glosses Sparse / disconnected graphs

Two-step hybrid approach already helped improve recallBut: No real combination of both notions

Idea: Combine them using Machine LearningExploit the complementary strengths more effectively


Setup - Features

Features:Gloss similarity (COS, PPR)Dijkstra-WSA distances Infinite distance if no target can be found

Other possible features:Part of speech, sense index, translation overlap, example sentence

patternsNo significant improvement by using them!

Þ Glosses and structure are sufficient


Setup - Classifiers

Classifiers used:Naive BayesBayesian NetworksPerceptronsSupport Vector Machines (SVMs)Decision Trees

Evaluation using 10-fold cross validationSame datasets as before


Evaluation

Random1:11st

SBDWSAHybrid

SVMNaive Bayes

Bayesian NetworkPerceptron

Decision Tree

Human performance


Evaluation

Random1:11st

SBDWSAHybrid

SVMNaive Bayes


Decision Tree

Human performance

General improvement in precision…


Evaluation

Random1:11st

SBDWSAHybrid

SVMNaive Bayes


Decision Tree

Human performance

…but in F-measure only for some of the

datasets!



Better overall results on 4 out of 8 datasetsMachine learning helps most for sparse and incomplete LSRs like

OmegaWiki and Wiktionary For „complete“ LSRs like WordNet, we cannot gain much

Better precision on 7 out of 8 Most robust: Bayesian NetworksComplex classifiers (e.g. SVMs) challenged by skewed values

Main source of improvements:Better classification of „borderline“ examplesHigh gloss similarity & distance or vice versa


Borderline Example

Genome:1. “The non-redundant genetic information stored in DNA sequences

that defines an individual organism”2. “In the context of a genetic algorithm, the information that defines

an individual entity”Very similar descriptionBut: Far apart in the graph => No alignment!

90

Motivation




Outline





Linked Lexical Resources

LLRs

Gurevych et al., EACL

2012

█ █

Eckle-Kohler et al., LREC

2012

█ █

Eckle-Kohler & Gurevych, EACL 2012

█

Eckle-Kohler et al., LMF,

2013

█ █ █

Eckle-Kohler et al., SWJ,

2014

█

█ Large-scale unified LR based on LMF

█ Standardizing heterogeneous LRs

█ Standardized format for subcat frames

█ Language independence of lexicon models

Iryna Gurevych, Judith Eckle-Kohler, Silvana Hartmann, Michael Matuschek, Christian M. Meyer and Christian Wirth: UBY - A Large-Scale Unified Lexical-Semantic Resource Based on LMF, in: Proceedings of the 13th Conference of the European chapter of the Association for Computational Linguistics (EACL), April 2012.


UBY: Linking Lexical Resource

Web 2.0

IMSLex-Subcat

UBY Two main characteristics:- Word Sense Alignments

- Standardized Representation


Heterogeneity of Lexical Resources

Complementary information types

Different terminology

Incompatible Data formats


Unified Lexical Resource UBY

Unified lexicon model

Extensible

Preserves variety of lexical information


Structure Integration

Standardized representation frameworks Lexical Markup Framework (LMF)

http://www.lexicalmarkupframework.org Text Encoding Initiative (TEI)

http://www.tei-c.org<entry> <form> <orth>disproof</orth> <pron>dIs"pru:f</pron> </form> <gramGrp> <pos>n</pos> </gramGrp> <sense n="1"> <def>facts that disprove something.</def> </sense> <sense n="2"> <def>the act of disproving.</def> [..]

http://www.lexicalmarkupframework.org/

http://www.tei-c.org/


Structure Integration in UBY

(Eck

le-K

oh

ler

et

al.

20

12

)

97

1. To sing: To produce musical or harmonious sounds with one’s voice.

2. To sing: To express audibly by means of a harmonious vocalization. 3. To sing: To confess under interrogation.

1. singen: Mit der Stimme harmonische Töne erzeugen.

1. To sing: Produce tones with the voice

2. To sing: divulge confidential information or secrets

1. To sing: To produce harmonious sounds with one's voice.


Sense Alignments Enable Semantic Interoperability

Senses linked by SenseAxis class (over 1,000,000 instances) English alignments, e.g. WordNet-Wikipedia German alignments, e.g. GermaNet-Wiktionary Cross-lingual alignments, e.g. WordNet-OmegaWiki DE


Available Alignments

Wikipedia English—WordNet 83,192

Wiktionary English—WordNet 138,282

GermaNet—Wiktionary German 32,850

FrameNet—Wiktionary English 12,340

Wiktionary English—OmegaWiki English 34,509

WordNet—OmegaWiki German 27,529

Wiktionary German—Wikipedia German 21,872

Wiktionary English—Wikipedia English 66,050

WordNet—VerbNet 40,716

FrameNet—VerbNet 17,529

Wikipedia English—OmegaWiki English 3,960

Wikipedia German—OmegaWiki German 1,097

Wikipedia English—Wikipedia German 463,311

OmegaWiki English—OmegaWiki German 58,785


Resource Integration Workflow in UBY

JWNL FN API JWPL JWKTL

Human users Machines


Step 1. Structure Integration

UBY API UBY API UBY API UBY API


UBY

101

UBY-API


Step 2. Content Integration


UBY


UBY Web UI – Textual View

Textual View: allows to list senses across all resources, to display sense details and to perform sense comparisons.


UBY Web UI – Visual View

Visual view: allows to explore the sense alignments.


UBY Java API

The UBY API is open source at Google Code: http://code.google.com/p/uby/

Getting Started:1. Download a UBY database dump2. Import the dump into a MySQL database3. Start using the UBY API

The UBY API is work in progress! Many API methods need to be added – consider contributing!

107

http://uby.ukp.informatik.tu-darmstadt.de/uby/UBY


UBY – Data and Tools

http://code.google.com/p/uby/

https://uby.ukp.informatik.tu-darmstadt.de/webui/

Web Interface

Open Source API (JAVA)

Database Dumps UBY

108

Motivation




Outline

Joint Approaches to Word Sense Alignment




Utilizing Linked Lexical Resources

Utilizing LLRs

Cholakov et al., EACL

2014

█

Matuschek et al.,

KONVENS 2014

█

Matuschek et al., TC3,

2013

█


█

Hartmann et al., 2014 (in preparation)

█

█ Sense annotation/disambiguation

█ Machine/computer-assisted translation

█ Semantic role labelling

█ Cross-language transfer of lexical-semantic resources

Michael Matuschek and Tristan Miller and Iryna Gurevych : A Language-independent Sense Clustering Approach for Enhanced WSD, in Proceedings of the 12th Konferenz zur Verarbeitung naturlicher Sprache (KONVENS 2014), to appear

Michael Matuschek and Christian M. Meyer and Iryna Gurevych: Multilingual Knowledge in Aligned Wiktionary and OmegaWiki for Translation Applications, in: Translation: Corpora, Computation, Cognition (TC3), vol. 3, no. 1, p. 87-118, July 2013

Kostadin Cholakov and Judith Eckle-Kohler and Iryna Gurevych: Automated Verb Sense Labelling Based on Linked Lexical Resources, in: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014), pp. 68-77, April 2014

110April 28, 2014 | Computer Science Department | UKP Lab Prof. Iryna Gurevych | Dr. Judith Eckle-Kohler

Automatic Verb Sense Labelling of Corpora

Motivation Automatically create verb sense-annotated corpora as training data for

supervised approachesApproach1. Create sense patterns from UBY (combining WordNet, FrameNet, VerbNet,

Wiktionary)2. Compare these to patterns derived from corpus instances3. Assign word sense in corpus if similarity is above a threshold4. Use this data to train supervised systems (distant supervision)Results Significant improvement over MFS baseline for verb sense disambiguation on

MASC and Senseval-3


Using Alignments for Word Sense Clustering

Motivation Cluster fine-grained word senses in expert-built resources to improve WSD

performanceApproach1. Create alignments between resources using Dijkstra-WSA, allowing 1:n

alignments Source: GermaNet, WordNet Target: Wiktionary, Wikipedia, OmegaWiki

2. If two or more senses are aligned to the same sense in the other resource, merge them into one coarse sense

3. Rescore state-of-the-art WSD algorithms on clustered sense inventoryResults Significant improvement over random clusters of same granularity on WebCAGe

(GermaNet) and Senseval-3 (WordNet)


Using Aligned Resources for Computer-aided Translation

Motivation SMT systems help, but are not smart enough to replace manual translationApproach1. Create sense alignments between multilingual resources2. Display information from all resources for a particular meaningResults Substantially more available translations and other information types Example: “bass” in Wiktionary and OmegaWiki


Programming language is not an island!

Word Sense Alignment is vital for increasing coverage and richness of sense representations

But: It is a hard problem!Various approaches Similarity-based, graph-based, combined

Performance depends on resources Sparsity, availability of glosses,…Machine learning shows most robust results

Aligned resources help improve performance for various applicationsVSD, coarse-grained WSD, computer-aided translation


Future Work

1. Linked lexical resources (LLRs) Integrating and aligning further resources in UBY Special focus: cross-lingual alignment

2. Construction of aligned lexical resources Investigating more elaborate similarity measures for glosses Using different graph algorithms to better express similarity Aligning several resources at once (n-way alignment)

3. Utilizing LLR for language processing Unified deep learning framework utilizing linked resources Distant supervision applied to semantic role labeling Word sense disambiguation and lexical substitution for German


Thank you. Questions?


Sense Alignment of Lexical Resources(References)

Elisabeth Niemann and Iryna Gurevych. The People’s Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and WordNet. In: Proceedings of the 9th International Conference on Computational Semantics (IWCS), p. 205-214, January 2011.

Christian M. Meyer and Iryna Gurevych. What Psycholinguists Know About Chemistry: Aligning Wiktionary and WordNet for Increased Domain Coverage. In: Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP), p. 883–892, November 2011.

Michael Matuschek and Iryna Gurevych. Dijkstra-WSA: A Graph-Based Approach to Word Sense Alignment. Transactions of the Association for Computational Linguistics (TACL), vol. 1, p. 151-164, May 2013.

Silvana Hartmann and Iryna Gurevych. FrameNet on the Way to Babel: Creating a Bilingual FrameNet Using Wiktionary as Interlingual Connection. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), vol. 1, p. 1363-1373, August 2013.

Tristan Miller and Iryna Gurevych. WordNet-Wikipedia-Wiktionary: Construction of a Three-way Alignment. In: Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC), May 2014. (to appear)


Linked Lexical Resources @ UKP(References)

Judith Eckle-Kohler and Iryna Gurevych. Subcat-LMF – Fleshing out a Standardized Format for Subcategorization Frame Interoperability. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL), p. 550-560, April 2012.

Judith Eckle-Kohler, Iryna Gurevych, Silvana Hartmann, Michael Matuschek and Christian M. Meyer. UBY-LMF - A Uniform Model for Standardizing Heterogeneous Lexical-Semantic Resources in ISO-LMF. In: Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC), p. 275-282, May 2012.

Judith Eckle-Kohler, Iryna Gurevych, Silvana Hartmann, Michael Matuschek and Christian M. Meyer. UBY-LMF - Exploring the Boundaries of Language-Independent Lexicon Models. In: LMF Lexical Markup Framework, chap. 10, p. 145-156, ISTE - HERMES - Wiley, 2013. ISBN 978 184 821 4309.

Iryna Gurevych, Judith Eckle-Kohler, Silvana Hartmann, Michael Matuschek, Christian M. Meyer and Christian Wirth. UBY - A Large-Scale Unified Lexical-Semantic Resource Based on LMF. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL), p. 580--590, April 2012.

Judith Eckle-Kohler, John Philip McCrae, and Christian Chiarcos. lemonUby - A Large, Interlinked, Syntactically-rich Lexical Resource for Ontologies. Semantic Web Journal, March 2014.


Utilizing Linked Lexical Resources(References)

Kostadin Cholakov, Judith Eckle-Kohler, and Iryna Gurevych. Automated Verb Sense Labelling Based on Linked Lexical Resources. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL), p. 68-77, April 2014.

Silvana Hartmann and Iryna Gurevych. FrameNet on the Way to Babel: Creating a Bilingual FrameNet Using Wiktionary as Interlingual Connection. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), vol. 1, p. 1363-1373, August 2013.

Michael Matuschek, Tristan Miller, and Iryna Gurevych. A Language-independent Sense Clustering Approach for Enhanced WSD. In: Proceedings of the 19th Conference on Empirical Methods in Natural Language Processing, October 2014. (in submission)

Michael Matuschek, Christian M. Meyer, and Iryna Gurevych. Multilingual Knowledge in Aligned Wiktionary and OmegaWiki for Translation Applications. Translation: Corpora, Computation, Cognition (TC3), vol. 3, no. 1, p. 87-118, July 2013.

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