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Methodology

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Comparison of the Baseline Knowledge-, Corpus-, and Web-based Similarity Measures for Semantic Relations Extraction Alexander Panchenko [email protected] Center for Natural Language Processing (CENTAL) Université catholique de Louvain, Belgium

31 July 2011 / GEMS 2011

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Plan

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Semantic Relations

r = hci , t, cj i – semantic relation, where ci , cj ∈ C, t ∈ T C – concepts e.g. radio or receiver operating characteristic T – semantic relation types, e.g. hyponymy or synonymy R ⊆ C × T × C – set of semantic relations

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Semantic Relations Example: BLESS Parameters: 200 source concepts Cs 8625 destination concepts Cd each concept c ∈ {Cr ∪ Cd } is a single English word T = { hyper, coord, mero, event, attri, random } 26554 semantic relations R ⊆ Cs × T × Cd

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Semantic Relations Example: BLESS Parameters: 200 source concepts Cs 8625 destination concepts Cd each concept c ∈ {Cr ∪ Cd } is a single English word T = { hyper, coord, mero, event, attri, random } 26554 semantic relations R ⊆ Cs × T × Cd Examples, R: halligator, coord, snakei hfreezer, attri, emptyi hphone, hyper, devicei hradio, mero, headphonei heagle, random, awardi Alexander Panchenko

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Another Example: Information Retrieval Thesaurus

Figure: A part of a the information retrieval thesaurus EuroVoc.

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Another Example: Information Retrieval Thesaurus

Figure: A part of a the information retrieval thesaurus EuroVoc.

R= henergy-generating product, NT, energy industryi henergy technology, NT, energy industryi hpetrolium, RT, fossil fueli henergy technology, RT, oil technologyi ... Alexander Panchenko

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Problem Semantic Relations Extraction Method Input: lexically expressed concepts C, semantic relation types T ^∼R Ouput: lexico-semantic relations R

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Problem Semantic Relations Extraction Method Input: lexically expressed concepts C, semantic relation types T ^∼R Ouput: lexico-semantic relations R Solutions: Pattern-based methods

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Problem Semantic Relations Extraction Method Input: lexically expressed concepts C, semantic relation types T ^∼R Ouput: lexico-semantic relations R Solutions: Pattern-based methods Manually constructed patterns (Hearst, 1992) Semi-automatically constructed patterns (Snow et al., 2004) Unsupervised patterns learning (Etzioni et al., 2005)

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Problem Semantic Relations Extraction Method Input: lexically expressed concepts C, semantic relation types T ^∼R Ouput: lexico-semantic relations R Solutions: Pattern-based methods Manually constructed patterns (Hearst, 1992) Semi-automatically constructed patterns (Snow et al., 2004) Unsupervised patterns learning (Etzioni et al., 2005)

Unsupervised similarity-based methods (Lin, 1998; Sahlgren, 2006)

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Problem Semantic Relations Extraction Method Input: lexically expressed concepts C, semantic relation types T ^∼R Ouput: lexico-semantic relations R Solutions: Pattern-based methods Manually constructed patterns (Hearst, 1992) Semi-automatically constructed patterns (Snow et al., 2004) Unsupervised patterns learning (Etzioni et al., 2005)

Unsupervised similarity-based methods (Lin, 1998; Sahlgren, 2006)

Research Questions w.r.t. similarity-based methods: Which similarity measure is the best for relations extraction? Alexander Panchenko

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Introduction

Methodology

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Problem Semantic Relations Extraction Method Input: lexically expressed concepts C, semantic relation types T ^∼R Ouput: lexico-semantic relations R Solutions: Pattern-based methods Manually constructed patterns (Hearst, 1992) Semi-automatically constructed patterns (Snow et al., 2004) Unsupervised patterns learning (Etzioni et al., 2005)

Unsupervised similarity-based methods (Lin, 1998; Sahlgren, 2006)

Research Questions w.r.t. similarity-based methods: Which similarity measure is the best for relations extraction? Do various measures capture relations of the same type? Alexander Panchenko

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Discussion

Introduction

Methodology

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Motivation: Automatic Thesaurus Construction

Figure: A technology for automatic thesaurus construction.

Alexander Panchenko

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Discussion

Introduction

Methodology

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Motivation: Automatic Thesaurus Construction

Figure: A technology for automatic thesaurus construction.

Applications: Query expansion and query suggestion

Alexander Panchenko

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Discussion

Introduction

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Motivation: Automatic Thesaurus Construction

Figure: A technology for automatic thesaurus construction.

Applications: Query expansion and query suggestion Navigation and browsing on the corpus

Alexander Panchenko

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Discussion

Introduction

Methodology

Results

Motivation: Automatic Thesaurus Construction

Figure: A technology for automatic thesaurus construction.

Applications: Query expansion and query suggestion Navigation and browsing on the corpus Visualization of the corpus

Alexander Panchenko

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Discussion

Introduction

Methodology

Results

Motivation: Automatic Thesaurus Construction

Figure: A technology for automatic thesaurus construction.

Applications: Query expansion and query suggestion Navigation and browsing on the corpus Visualization of the corpus ... Alexander Panchenko

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The Contributions

Studying 21 corpus-, knowledge-, and web-based measures

Alexander Panchenko

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The Contributions

Studying 21 corpus-, knowledge-, and web-based measures Using the BLESS dataset

Alexander Panchenko

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The Contributions

Studying 21 corpus-, knowledge-, and web-based measures Using the BLESS dataset Analysis of the semantic relation types

Alexander Panchenko

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Introduction

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The Contributions

Studying 21 corpus-, knowledge-, and web-based measures Using the BLESS dataset Analysis of the semantic relation types Reporting empirical relation distributions

Alexander Panchenko

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Discussion

Introduction

Methodology

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The Contributions

Studying 21 corpus-, knowledge-, and web-based measures Using the BLESS dataset Analysis of the semantic relation types Reporting empirical relation distributions Finding most and least similar measures

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Introduction

Methodology

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Discussion

Similarity-based Semantic Relations Extraction Semantic Relations Extraction Algorithm

1 2 3 4

Input: Concepts C, Parameters of similarity measure P, Threshold k, Min.similarity value γ ^ Output: Unlabeled semantic relations R S ← sim(C, P) ; S ← normalize(S) ; ^ ← threshold(S, k, γ) ; R ^; return R

Alexander Panchenko

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Introduction

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Discussion

Similarity-based Semantic Relations Extraction Semantic Relations Extraction Algorithm

1 2 3 4

Input: Concepts C, Parameters of similarity measure P, Threshold k, Min.similarity value γ ^ Output: Unlabeled semantic relations R S ← sim(C, P) ; S ← normalize(S) ; ^ ← threshold(S, k, γ) ; R ^; return R sim – one of 21 tested similarity measures

Alexander Panchenko

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Introduction

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Discussion

Similarity-based Semantic Relations Extraction Semantic Relations Extraction Algorithm

1 2 3 4

Input: Concepts C, Parameters of similarity measure P, Threshold k, Min.similarity value γ ^ Output: Unlabeled semantic relations R S ← sim(C, P) ; S ← normalize(S) ; ^ ← threshold(S, k, γ) ; R ^; return R sim – one of 21 tested similarity measures normalize – similarity score normalization

Alexander Panchenko

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Introduction

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Discussion

Similarity-based Semantic Relations Extraction Semantic Relations Extraction Algorithm

1 2 3 4

Input: Concepts C, Parameters of similarity measure P, Threshold k, Min.similarity value γ ^ Output: Unlabeled semantic relations R S ← sim(C, P) ; S ← normalize(S) ; ^ ← threshold(S, k, γ) ; R ^; return R sim – one of 21 tested similarity measures normalize – similarity score normalization threshold – kNN thresholding function S|C| R = i=1 {hci , t, cj i : cj ∈ top k% concepts ∧ sij ≥ γ} . Alexander Panchenko

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0), corpus (S EM C OR).

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0), corpus (S EM C OR). Variables: h – the height of the network

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0), corpus (S EM C OR). Variables: h – the height of the network len(ci , cj ) – length of the shortest path between concepts

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0), corpus (S EM C OR). Variables: h – the height of the network len(ci , cj ) – length of the shortest path between concepts P(c) – probability of the concept, estimated from a corpus

Alexander Panchenko

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Discussion

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0), corpus (S EM C OR). Variables: h – the height of the network len(ci , cj ) – length of the shortest path between concepts P(c) – probability of the concept, estimated from a corpus

Inverted Edge Count: sij = len(ci , cj )−1

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0), corpus (S EM C OR). Variables: h – the height of the network len(ci , cj ) – length of the shortest path between concepts P(c) – probability of the concept, estimated from a corpus

Inverted Edge Count: sij = len(ci , cj )−1 Leacock-Chodorow: sij = −log Alexander Panchenko

len(ci , cj ) 2h 10/31

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Discussion

Knowledge-based Measures (8) Wu-Palmer sij =

2 · len(cr , lcs(ci , cj )) len(ci , lcs(ci , cj )) + len(cj , lcs(ci , cj )) + 2 · len(croot , lcs(ci , cj ))

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Knowledge-based Measures (8) Wu-Palmer sij =

2 · len(cr , lcs(ci , cj )) len(ci , lcs(ci , cj )) + len(cj , lcs(ci , cj )) + 2 · len(croot , lcs(ci , cj ))

Resnik: sij = −log(P(lcs(ci , cj )))

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Knowledge-based Measures (8) Wu-Palmer sij =

2 · len(cr , lcs(ci , cj )) len(ci , lcs(ci , cj )) + len(cj , lcs(ci , cj )) + 2 · len(croot , lcs(ci , cj ))

Resnik: sij = −log(P(lcs(ci , cj ))) Jiang-Conrath: sij = [2 · log(P(lcs(ci , cj ))) − log(P(ci ) − log(P(cj ))]−1

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Knowledge-based Measures (8) Wu-Palmer sij =

2 · len(cr , lcs(ci , cj )) len(ci , lcs(ci , cj )) + len(cj , lcs(ci , cj )) + 2 · len(croot , lcs(ci , cj ))

Resnik: sij = −log(P(lcs(ci , cj ))) Jiang-Conrath: sij = [2 · log(P(lcs(ci , cj ))) − log(P(ci ) − log(P(cj ))]−1 Lin: sij =

2 · log(P(lcs(ci , cj ))) log(P(ci ) + log(P(cj ))

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0).

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0). Variables: gloss(c) – definition of the concept

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0). Variables: gloss(c) – definition of the concept sim(gloss(ci ), gloss(cj )) – similarity of concepts’ glosses

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0). Variables: gloss(c) – definition of the concept sim(gloss(ci ), gloss(cj )) – similarity of concepts’ glosses fi – context vector of ci , calculated on the corpus of all glosses

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0). Variables: gloss(c) – definition of the concept sim(gloss(ci ), gloss(cj )) – similarity of concepts’ glosses fi – context vector of ci , calculated on the corpus of all glosses

Extended Lesk (Banerjee and Pedersen, 2003): X X sim(gloss(ci ), gloss(cj )), where Ci = {c : ∃ hc, t, ci i}. sij = ci ∈Ci cj ∈Cj

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Knowledge-based Measures Description Data: semantic network (W ORD N ET 3.0). Variables: gloss(c) – definition of the concept sim(gloss(ci ), gloss(cj )) – similarity of concepts’ glosses fi – context vector of ci , calculated on the corpus of all glosses

Extended Lesk (Banerjee and Pedersen, 2003): X X sim(gloss(ci ), gloss(cj )), where Ci = {c : ∃ hc, t, ci i}. sij = ci ∈Ci cj ∈Cj

Gloss Vectors (Patwardhan and Pedersen, 2006): sij =

X [ vi · vj , where vi = fj , where Gi = gloss(c) kvi k kvj k ∀j:cj ∈Gi

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c∈Ci

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Corpus-based Measures (4) Description Data: corpus (WAC YPEDIA (800M), UK WAC (2000M)).

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Corpus-based Measures (4) Description Data: corpus (WAC YPEDIA (800M), UK WAC (2000M)). Variables: fi – context vector for ci

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Corpus-based Measures (4) Description Data: corpus (WAC YPEDIA (800M), UK WAC (2000M)). Variables: fi – context vector for ci Cosine: sij =

Alexander Panchenko

fi · fj ; kfi k kfj k

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Corpus-based Measures (4) Description Data: corpus (WAC YPEDIA (800M), UK WAC (2000M)). Variables: fi – context vector for ci Cosine: sij = Jaccard: sij =

fi · fj ; kfi k kfj k

kmin(fi , fj )k1 ; kmax(fi , fj )k1

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Corpus-based Measures (4) Description Data: corpus (WAC YPEDIA (800M), UK WAC (2000M)). Variables: fi – context vector for ci Cosine: sij = Jaccard: sij =

fi · fj ; kfi k kfj k

kmin(fi , fj )k1 ; kmax(fi , fj )k1

Euclidian: sij = kfi − fj k ; Alexander Panchenko

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Corpus-based Measures (4)

Manhattan: sij = kfi − fj k1 .

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Web-based Measures (9) Description Data: number of the hits returned by an IR system (G OOGLE , YAHOO , YAHOO BOSS, FACTIVA).

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Web-based Measures (9) Description Data: number of the hits returned by an IR system (G OOGLE , YAHOO , YAHOO BOSS, FACTIVA). Variables: hi – number of hits returned by query "ci "

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Web-based Measures (9) Description Data: number of the hits returned by an IR system (G OOGLE , YAHOO , YAHOO BOSS, FACTIVA). Variables: hi – number of hits returned by query "ci " hij – number of hits returned by the query "ci AND cj "

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Web-based Measures (9) Description Data: number of the hits returned by an IR system (G OOGLE , YAHOO , YAHOO BOSS, FACTIVA). Variables: hi – number of hits returned by query "ci " hij – number of hits returned by the query "ci AND cj "

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Discussion

Introduction

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Web-based Measures (9) Description Data: number of the hits returned by an IR system (G OOGLE , YAHOO , YAHOO BOSS, FACTIVA). Variables: hi – number of hits returned by query "ci " hij – number of hits returned by the query "ci AND cj "

Normalized Google Distance (Cilibrasi and Vitanyi, 2007): sij =

max(log(hi ), log(hj )) − log(hij ) log(M) − min(log(hi ), log(hj ))

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Web-based Measures (9) Description Data: number of the hits returned by an IR system (G OOGLE , YAHOO , YAHOO BOSS, FACTIVA). Variables: hi – number of hits returned by query "ci " hij – number of hits returned by the query "ci AND cj "

Normalized Google Distance (Cilibrasi and Vitanyi, 2007): sij =

max(log(hi ), log(hj )) − log(hij ) log(M) − min(log(hi ), log(hj ))

PMI-IR (Turney, 2001):

P P hij i j hi hj P(ci , cj ) P sij = −log = −log . P(ci )P(cj ) hi hj i hij Alexander Panchenko

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"Theoretical" Classification of the Similarity Measures

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General Performance Evaluation Protocol Precision =

^| |R∩R ^ | , Recall |R

=

^| |R∩R |R| , F1

=2·

R – all relations from BLESS, but random ^ – extracted relations R

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Precision·Recall Precision+Recall

Discussion

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General Performance Evaluation Protocol Precision =

^| |R∩R ^ | , Recall |R

=

^| |R∩R |R| , F1

=2·

R – all relations from BLESS, but random ^ – extracted relations R

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Precision·Recall Precision+Recall

Discussion

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General Performance: Scores @ Precision = 0.80

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General Performance: Learning curve of the BDA-Cos

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General Performance: Learning curve of the BDA-Cos

∼ 0.44 ∆F11M−10M =

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General Performance: Learning curve of the BDA-Cos

∼ 0.44 ∆F11M−10M = ∼ 0.16 ∆F110M−100M = Alexander Panchenko

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General Performance: Learning curve of the BDA-Cos

∼ 0.44 ∆F11M−10M = ∼ 0.16 ∆F110M−100M = ∼ 0.03 ∆F1100M−1000M = Alexander Panchenko

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Example of the Extracted Relations (BDA-Cos)

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Comparing Relation Distributions Evaluation Protocol Percent =

^t R ^| |R∩R

· 100

^ t is a set of extracted relations of type t, R

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S

t∈T

^ t = |R ∩ R ^| R

Introduction

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Discussion

Comparing Relation Distributions Evaluation Protocol Percent =

^t R ^| |R∩R

· 100

^ t is a set of extracted relations of type t, R

S

Issue: high sensitivity of the Percent to k:

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t∈T

^ t = |R ∩ R ^| R

Introduction

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Comparing Relation Distributions: Scores @ k = 10%

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Comparing Relation Distributions: Scores @ k = 40%

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Comparing Relation Distributions: Scores

Similarity to the BLESS: Random measure: χ2 = 5.36, p = 0.252 21 measures: χ2 = 89.94 − 4000, p < 0.001

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Comparing Relation Distributions: Scores

Similarity to the BLESS: Random measure: χ2 = 5.36, p = 0.252 21 measures: χ2 = 89.94 − 4000, p < 0.001

Independence of the Relation Distributions: 21 measures: χ2 = 10487, p < 0.001, df = 80 knowledge-based measures: χ2 = 2529, df = 28, p < 0.001 corpus-based measures: χ2 = 245, df = 12, p < 0.001 web-based measures: χ2 = 3158, df = 32, p < 0.001

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Relation Distributions: Distribution of the Scores

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Relation Distributions: Most Similar Measures

Measures Dissimilarity Calculate distance xij between measures simi and simj : xij = xji =

X (|R^it | − |R^jt |)2 t∈ T

|R^jt |

R^it – correctly extracted relations of type t with measure simi

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Relation Distributions: Most Similar Measures

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Relation Distributions: Most Similar Measures Threshold the 21 × 21 matrix X: if xij < 220 then xij = 0 Visualize the distances with the Fruchterman-Reingold (1991) graph layout

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Discussion

Conclusion: General Performance Best knowledge-based measure – Resnik (W ORD N ET) Best corpus-based and the best measure – BDA-Cos (UK WAC) Best web-based measure – NGD-YAHOO Best measures clearly separate correct and random relations Relations Distributions All measures extract many co-hyponyms The measures were grouped according to similarity of their relation distributions The measures provide complimentary results

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Introduction

Methodology

Results

Further Research: Methods Develop a combined similarity measure – linear combination, logistic regression, committees... More measures – LSA, SDA, LDA, surface-based similarity, kernels, definition-based measures,... Working with MWE Classify extracted relations: hyponymy, synonymy, etc.

Alexander Panchenko

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Discussion

Introduction

Methodology

Results

Further Research: Methods Develop a combined similarity measure – linear combination, logistic regression, committees... More measures – LSA, SDA, LDA, surface-based similarity, kernels, definition-based measures,... Working with MWE Classify extracted relations: hyponymy, synonymy, etc. Evaluation Using a golden standard with synonyms Using a golden standard with MWE – thesauri Use Speirman’s correlation rs to compare the results An application-based evaluation – query expansion Alexander Panchenko

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Discussion

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Methodology

Results

Questions

Thank you! Questions?

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Discussion

Comparison of the Baseline Knowledge-, Corpus-, and ...

Center for Natural Language Processing (CENTAL). Université catholique de ... Extraction Method. Input: lexically expressed concepts C, semantic relation types T .... Description. Data: semantic network (WORDNET 3.0), corpus (SEMCOR).

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