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A Novel Discourse Parser Based on Support Vector Machine Classification. Source: ACL 2009 Author: David A. duVerle and Helmut Prendinger Reporter: Yong-Xiang Chen. Research problem. Automated annotation of a text with RST hierarchically organized relations To parse discourse

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a novel discourse parser based on support vector machine classification

A Novel Discourse Parser Based onSupport Vector Machine Classification

Source: ACL 2009

Author: David A. duVerle and Helmut Prendinger

Reporter: Yong-Xiang Chen

research problem
Research problem
  • Automated annotation of a text with RST hierarchically organized relations
    • To parse discourse
    • Within the framework of Rhetorical Structure Theory (RST)
    • Produce a tree-like structure
    • Based on SVM
rhetorical structure theory rst
Rhetorical Structure Theory (RST)
  • Mann and Thompson (1988)
  • A set of structural relations to composing units (‘spans’) of text
    • 110 distinct rhetorical relations
    • Relations can be of intentional, semantic, or textual nature
  • Two-step process (This study focus on step 2)
    • Segmentation of the input text into elementary discourse units (‘edus’)
    • Generation of the rhetorical structure tree
      • the edus constituting its terminal nodes
slide4
Edus:
    • Nucleus
      • relatively more important part of the text
    • Satellite
      • subordinate to the nucleus, represents supporting
      • information

out-going arrow

satellite

nucleu

satellite

nucleu

research restriction
Research restriction
  • A sequence of edus that have been segmented beforehand
  • Use the reduced set of 18 rhetorical relations
    • e.g.: PROBLEM-SOLUTION, QUESTION-ANSWER, STATEMENT-RESPONSE, TOPIC-COMMENT and COMMENT-TOPIC are all grouped under one TOPIC-COMMENT relation
  • Turned all n-ary rhetorical relations into nested binary relations
    • e.g.: LIST relation
  • Only adjacent spans of text can be put in relation within an RST tree (‘Principle of sequentiality’ (Marcu, 2000)
18 rhetorical relations
18 rhetorical relations
  • Attribution, Background, Cause, Comparison, Condition, Contrast, Elaboration, Enablement, Evaluation, Explanation, Joint, Manner-Means,

Topic-Comment, Summary, Temporal, Topic- Change, Textual-organization,same-unit

classifier
Classifier
  • Input: given two consecutive spans (atomic edus or RST sub-trees) from input text
  • Score the likelihood of a direct structural relation as well as probabilities for
    • a relation’s label
    • Nuclearity
  • Gold standard: human cross-validation levels
two separate classifiers
Two separate classifiers
  • to train two separate classifiers:
  • S: A binary classifier, for structure
    • existence of a connecting node between the two input sub-trees
  • L: A multi-class classifier, for rhetorical relation and nuclearity labeling
produce a valid tree
Produce a valid tree
  • Using these classifiers and a straight-forward bottom-up tree-building algorithm
classes
Classes
  • 18 super-relations and 41 classes
  • Considering only valid nuclearity options
    • e.g., (ATTRIBUTION, N, S) and (ATTRIBUTION, S, N) are two classes of ATTRIBUTION
    • but not (ATTRIBUTION, N, N)
reduce the multi classification
Reduce the multi-classification
  • Reduce the multi-classification problem through a set of binary classifiers, each trained either on a single class (“one vs. all”) or by pair (“one vs. one”)
input data
Input data
  • Annotated documents taken from the RST-DT corpus
    • paired with lexicalized syntax trees (LS Trees) for each sentence
    • a separate test set is used for performance evaluation
lexicalized syntax trees ls trees
Lexicalized syntax trees (LS Trees)
  • Taken directly from the Penn Treebank corpus then “lexicalized” using a set of canonical head-projection rules
    • tagged with lexical “heads” on each internal node of the syntactic tree
algorithm
Algorithm
  • Repeatedly applying the two classifiers and following a naive bottom-up tree-construction method
    • obtain a globally satisfying RST tree for the entire text
  • Starts with a list of all atomic discourse sub-trees
    • made of single edus in their text order
  • Recursively selects the best match between adjacent sub-trees
    • using binary classifier S
  • Labels the newly created sub-tree (using multi-label classifier L) and updates scoring for S, until only one sub-tree is left
features
Features
  • ‘S[pan]’ are sub-tree-specific features
    • Symmetrically extracted from both left and right candidate spans
  • ‘F[ull]’ are a function of the two sub-trees considered as a pair
textual organization
Textual Organization
  • S features:
    • Number of paragraph boundaries
    • Number of sentence boundaries
  • F features:
    • Belong to same sentence
    • Belong to same paragraph
  • Hypothesize a correlation between span length and rhetorical relation
    • e.g. the satellite in a CONTRAST relation will tend to be shorter than the nucleus
    • span size and positioning
      • using either tokens or edus as a distance unit
      • using relative values for positioning and distance
lexical clues and punctuation
Lexical Clues and Punctuation
  • Discourse markers are good indications
  • Use an empirical n-gram dictionary (for n∈ {1, 2, 3}) built from the training corpus and culled by frequency
    • Reason: Takes into account non-lexical signals such as punctuation
  • Counted and encoded n-gram occurrences while considering only the first and last n tokens of each span
    • Classifier accuracy improved by more than 5%
simple syntactic clues
Simple Syntactic Clues
  • For achieving better generalization
    • smaller dependency on lexical content
  • Add shallow syntactic clues by encoding part-of-speech (POS) tags for both prefix and suffix in each span
    • length higher than n = 3 did not seem to improve
dominance sets
Dominance Sets
  • Extract from the syntax parse trees
  • EX. Difficult to identify the scope of the ATTRIBUTION relation below:
one dominance logical nesting order
One dominance: Logical nesting order
  • Logical nesting order: 1A > 1B > 1C
  • This order allows us to favor the relation between 1B and 1C over a relation between 1A and 1B
dominance sets1
Dominance Sets
  • S features:
    • Distance to root of the syntax tree
    • Distance to common ancestor in the syntax tree
    • Dominating node’s lexical head in span
    • Relative position of lexical head in sentence
  • F features:
    • Common ancestor’s POS tag
    • Common ancestor’s lexical head
    • Dominating node’s POS tag (diamonds in Figure )
    • Dominated node’s POS tag (circles in Figure )
    • Dominated node’s sibling’s POS tag (rectangles in Figure )
rhetorical sub structure
Rhetorical Sub-structure
  • Structural features for large spans (higher-level relations)
  • Encoding each span’s rhetorical sub-tree into the feature vector
evaluation
Evaluation
  • Raw performance of SVM classifiers
  • Entire tree-building task
  • Binary classifier S
    • trained on 52,683 instances
      • Positive: 1/3, Negative:2/3
    • tested on 8,558 instances
  • classifier L
    • trained on 17,742 instances
      • labeled across 41 classes
    • tested on 2,887 instances

Baseline

baseline reitter s result 2003
Baseline: Reitter’s result 2003
  • A smaller set of training instances
    • 7976 v.s. 17,742 in this case
  • Less classes
    • 16 rhetorical relation labels with no nuclearity, v.s. to our 41 nuclearized relation classes
full system performance
Full System Performance
  • Comparing structure and labeling of the RST tree produced to that manual annotation
    • perfectly-segmented & SPADE segmenter output
    • blank tree structure (‘S’)
    • with nuclearity (‘N’)
    • with rhetorical relations (‘R’)
    • fully labeled structure (‘F’)
background
Background
  • Coherence relations reflect the authors intent
    • Hierarchically structured set of Coherence relations
  • Discourse
    • Focuses on a higher-level view of text than sentence level
slide29
14
  • Due to small differences in the way they were tokenized and pre-treated, rhetorical tree and LST are rarely a perfect match: optimal alignment is found by minimizing edit distances between word sequences
features1
Features
  • Use n-fold validation on S and L classifiers to assess the impact of some sets of features on general performance and eliminate redundant features
  • ‘S[pan]’ are sub-tree-specific features
    • Symmetrically extracted from both left and right candidate spans
  • ‘F[ull]’ are a function of the two sub-trees considered as a pair