Automatic classification for implicit discourse relations

Lin Ziheng

PDTB and discourse relations Explicit relations

Arg1: The bill intends to restrict the RTC to Treasury borrowings only, Arg2: unless the agency receives specific congressional authorization. (Alternative) (wsj_2200)

Implicit relations Arg1: The loss of more customers is the latest in a string

of problems. Arg2: [for instance] Church's Fried Chicken Inc. and

Popeye's Famous Fried Chicken Inc., which have merged, are still troubled by overlapping restaurant locations.

(Instantiation) (wsj_2225)

EXPANSION Conjunction Instantiation Restatement specification equivalence generalization Alternative conjunctive disjunctive chosen alternative Exception ListCOMPARISON Contrast juxtaposition opposition Pragmatic Contrast Concession expectation contra-expectation Pragmatic Concession

CONTINGENCY Cause reason result Pragmatic Cause justification Condition hypothetical general unreal present unreal past factual present factual past Pragmatic Condition relevance implicit assertionTEMPORAL Synchronous Asynchronous precedence succession

PDTB and discourse relations (2) PDTB hierarchy of relation classes, types and

subtypes

Level-1 classes Level-2 types Training instances

% Adjusted %

TEMPORAL Asynchronous 583 4.36 4.36

  Synchrony 213 1.59 1.59

CONTINGENCY Cause 3426 25.61 25.63

  Pragmatic Cause 69 0.52 0.52

  Condition 1 0.01  

  Pragmatic Condition 1 0.01  

COMPARISON Contrast 1656 12.38 12.39

  Pragmatic Contrast 4 0.03  

  Concession 196 1.47 1.47

  Pragmatic Concession

1 0.01  

EXPANSION Conjunction 2974 22.24 22.25

  Instantiation 1176 8.79 8.8

  Restatement 2570 19.21 19.23

  Alternative 158 1.18 1.18

  Exception 2 0.01  

  List 345 2.58 2.58

 Total   13375    

 Adjusted total   13366     

PDTB and discourse relations (3) Level-2 relation types, on

implicit dataset from the training sections (sec. 2 - 21) Remove Condition,

Pragmatic Condition, Pragmatic Contrast, Pragmatic Concession and Exception

11 relation types remained Dominating types:

Cause Conjunction Restatement

Contextual features

Arg1: Tokyu Department Store advanced 260 to 2410. Arg2: [and] Tokyu Corp. was up 150 at 2890. (List) (wsj_0374)

Arg1: Tokyu Department Store advanced 260 to 2410. Tokyu Corp. was up 150 at 2890.Arg2: [and] Tokyu Construction gained 170 to 1610. (List) (wsj_0374)

r1.Arg1 r1.Arg2r2.Arg1

r2.Arg2

r1.Arg1 r1.Arg2 r2.Arg2

r1 r2

r2

r1

Shared argument

Fully embedded argument

r2.Arg1

Contextual features (2) For each relation curr, look at the surrounding two

relations prev and next, giving to a total of six features

Shared argument:1. prev.Arg2 = curr.Arg12. curr.Arg2 = next.Arg1

Fully embedded argument:1. prev embedded in curr.Arg12. next embedded in curr.Arg23. curr embedded in prev.Arg24. curr embedded in next.Arg1

First figure in previous slide where curr = r2

Second figure in previous slide where curr = r2

Syntactic Features

Arg1: "The HUD budget has dropped by more than 70% since 1980," argues Mr. Colton.

Arg2: [so] "We've taken more than our fair share. (Cause)

(wsj_2227)

Syntactic Features (2) Collect all production rules:

Ignore function tags, such as -TPC, -SBJ, -EXT From Arg1: S NP VP, NP DT NNP NN, VP VBZ VP, VP

VBN PP PP, PP IN NP, NP QP NN, QP JJ IN CD, NP CD, DT The, NNP HUD, NN budget, VBZ has, VBN dropped, IN by, JJ more, IN than, CD 70, NN %, IN since, CD 1980

From Arg2: S `` NP VP ., NP PRP, VP VBP VP, VP VBN NP, NP NP PP, NP JJR, PP IN NP, NP PRP$ JJ NN, `` ``, PRP We, VBP ‘ve, VBN taken, JJR more, IN than, PRP$ our, JJ fair, NN share, . .

Dependency features

Dependency features (2) Collect all words with dependency types from

their dependents From Arg1: budget det nn, dropped nsubj aux prep

prep, by pobj, than advmod, 70 quantmod, % num, since pobj, argues ccomp nsubj, Colton nn

From Arg2: taken nsubj aux dobj, more prep, than pobj, share poss amod

Lexical features Collect all word pairs from Arg1 and Arg2, i.e.,

all (wi, wj) where wi is a word from Arg1 and wj is a word from Arg2

Experiments Classifier: OpenNLP MaxEnt Training data: sections 2 – 21 Test data: section 23 Use Mutual Information(MI) to rank

features for production rules, dependency rules and word pairs separately

Majority baseline: 26.1%, where all instances are classified into Cause

50 100 150 200 500 1000 2000 250027

29

31

33

35

37

39

41 39.295

Production rulesDependency rulesWord pairs

Number of features

Accura

cy

Experiments (2) Use contextual features and one other

feature class context + production rules context + dependency rules context + word pairs

Experiments (3) With large numbers of features

context + all production rules: 36.68% context + all dependency rules: 27.94% context + 10,000 word pairs: 35.25%

Experiments (4) Combine all feature classes, got an accuracy

of 40.21%. The following shows that all feature classes

contribute to the performance

Production rules

Dependency rules

Word pairs

Context Accuracy

200 0 0 No 37.5979

200 0 0 Yes 38.3812

200 0 200 Yes 39.9478

200 150 200 Yes 40.2089