Transcript Identifying Expressions of Opinion in Context

Identifying Expressions of
Opinion in Context
Eric Breck and Yejin Choi and Claire Cardie
IJCAI 2007
Introduction
• Traditional information extraction: answer
questions about facts
• Extract answers to subjective questions:
how does X feel about Y?
• Subjective information extraction and
question answering will require techniques
to analyze text below the sentence level
Introduction:
System Requirement
• Is its polarity positive, negative, or neutral?
• With what strength or intensity is the
opinion expressed: mild, medium, strong or
extreme?
• Who or what is the source, or holder, of the
opinion?
• What is its target, i.e. what is the opinion
about?
Introduction: Examples
• Minister Vedrine criticized the White House
reaction.
– the agent role = “Minister Vedrine”
– the object/theme role = “White House reaction”
• 17 persons were killed by sharpshooters faithful
to the president.
• Tsvangirai said the election result was
“illegitimate” and a clear case of “highway
robbery”.
• Criminals have been preying on Korean travelers
in China.
Introduction
• Direct subjective expressions (DSEs)
– criticized, faithful to
– Said (speech event, if subjective)
• Expressive subjective elements (ESEs)
– illegitimate, highway robbery
– preying on (instead of mugging)
• None has directly tackled the problem of
opinion expression identification.
Subjective Expressions
• The expressions can vary in length from one word
to over twenty words.
• They may be verb phrases, noun phrases, or
strings of words that do not correspond to any
linguistic constituent.
• Subjectivity is a realm of expression where writers
get quite creative, so no short fixed list can capture
all expressions of interest.
• Also, an expression which is subjective in one
context is not always subjective in another context.
Approach
• This task is treated as a tagging problem.
• Conditional random field
• Class variable
– IOB vs IO
• Features
• A linear-chain conditional random field is
chosen, using MALLET toolkit.
Features (1)
• Lexical features
– The word at position i relative to the current token.
– Lex-4 ~ Lex4, , 18,000 binary features per position
(vocabulary size)
• Syntactic features
– POS (45 binary features)
– prev, cur, next (CASS partial parser, constituent type),
100 binary features each.
• Dictionary-based features
Features (2)
• Dictionary-based features: 4 sources
– WordNet: WordNet hypernyms (29,989 binary
features)
– Levin: Levin’s categorization of English words
– Framenet: word in the categorization of nouns
and verbs in Framenet
– Wilson clues (subjective): strong or weak (two
binary features)
Statistics of Data
MPQA corpus, 535 documents.
135 for training, 400 for testing.
10-fold cross validation
Evaluation
• Metric: Precision/Recall/F-measure
– Exact
– Overlap
• Baselines: dictionary-based
– two dictionaries of subjectivity clues: Wiebe vs.
Wilson
– Wilson is incorporated in this experiment
Results (DSE/ESE)
Results (DSE and ESE)
Results (Dictionary-based)
• WordNet is the most useful
• The other dictionaries only help a little
Discussion
• Rules of boundary agreement is not defined
for the annotations: order 1 outperform order
0
• DSEs includes speech events like “said” or
“a statement”, which may be objective.
• Expressions of subjectivity tend to cluster,
therefore density-based features might help.
• Inter-annotator agreement of DSE: 0.75;
ESE:0.72