An Investigation of
Implicatures in Chinese
Lingjia Deng, Janyce Wiebe
Intelligent Systems Program
Department of Computer Science
University of Pittsburgh
Outline
 Introduction
 Implicature in Chinese
 Inference in Chinese
 Extracting Chinese GoodFor/BadFor
 Chinese GoodFor/BadFor Words
 Syntax of Chinese Agents/Objects
 Chinese Sentiment Analysis
 Conclusions
Outline
 Introduction
 Implicature in Chinese
 Inference in Chinese
 Extracting Chinese GoodFor/BadFor
 Chinese GoodFor/BadFor Words
 Syntax of Chinese Agents/Objects
 Chinese Sentiment Analysis
 Conclusions
Introduction
 Scenario:
 The government proposes the bill of Affordable
Care Act. We want to analyze everyone’s
opinion of it.
 We can collect opinions by doing survey,
questionnaire, etc.
 We can also collect the writer’s stances by
analyzing their posts online.
Introduction
 “The bill will lower the skyrocketing healthcare
costs.”
 Explicit (Direct) Sentiment:
 writer negative toward the skyrocketing healthcare
costs
 The healthcare cost is too high. I cannot afford it.
 Implicit (Inferred) Sentiment:
 writer positive toward the bill will lower costs
 There is a chance that the costs could be decreased!
I love it!
 writer positive toward the bill
 The bill is able to do this! I’ll vote for it!
GoodFor/BadFor Event
 “The bill will lower the skyrocketing healthcare costs.”
 <bill, lower, healthcare costs>
Benefactive/Malefactive
Event
 GoodFor/BadFor
Event (Deng et al., ACL
2013 short):
 goodFor event: help, increase, etc
 badFor event: lower, destroy, decrease, etc
 <agent, goodFor/badFor event, object>
 GoodFor/BadFor Corpus (Deng et al., ACL 2013 short):
 134 political editorials
 e.g. <bill, lower, healthcare costs>
 e.g. <positive, badFor, negative>
 almost 20% sentences have clear goodFor/badFor events
 available at mpqa.cs.pitt.edu
Related Work
 Words/Phrases directly imply implicit opinions. (Zhang and
Liu, 2011; Feng et al., 2013)
 Infer an overall polarity of a sentence by compositional
semantics. (Choi and Cardie, 2008; Moilanen et al., 2010)
 Identify classes of goodFor/badFor terms, and carry out
studies involving artificially constructed goodFor/badFor
triples and corpus examples matching fixed linguistic
templates. (Anand and Reschke 2010; 2011)
 Generate a lexicon of patient polarity verbs, which
correspond to goodFor/badFor events whose spans are
verbs. (Goyal et al., 2012)
 Investigate sarcasm where the writer holds a positive
sentiment toward a negative situation. (Riloff et al., 2013)
Our Work of GoodFor/BadFor
 An annotated goodFor/badFor Corpus. (Deng et al., ACL
2013 short)
 A sense-level goodFor/badFor lexicon. (Choi et al., WASSA
2014)
 Four inference rule schemas and a graph-based model
for sentiment propagation. (Deng and Wiebe, EACL 2014)
 An optimization framework for joint sentiment inference
and disambiguating goodFor/badFor components. (Deng
et al., Coling 2014)
 A rule-based framework for representing and analyzing
opinion implicatures. (Wiebe and Deng, arXiv 2014;
WASSA 2014)
Motivation For This Work
 This work is investigation of implicatures in Chinese.
 People speaking different languages may express
their opinions in different ways.
 Before directly applying goodFor/badFor
implicature in English to Chinese, we want to
investigate:
 whether such implicature also exists in Chinese;
 whether the sentiment inference rules also apply to
Chinese implicit opinions;
 whether it is feasible to extract goodFor/badFor events
and the corresponding components in Chinese.
Outline
 Motivation
 Implicature in Chinese
 Agreement Study
 Inference in Chinese
 Extracting Chinese GoodFor/BadFor
 Chinese GoodFor/BadFor Words
 Syntax of Chinese Agents/Objects
 Chinese Sentiment Analysis
 Conclusions
Implicature in Chinese:
Agreement Study
 An opinion-orientated, paragraph-paralleled
corpus: Chinese version of the New York Times
(http://cn.nytimes.com/).
 Select the English paragraphs containing English
goodFor/badFor words.
 Present the parallel Chinese paragraphs.
Implicature in Chinese:
Agreement Study
 All the three annotators, including me, are
Chinese graduate students in University of
Pittsburgh.
 Annotate 60 paragraphs, 253 sentences.
 Conduct the agreement study in the same
manner with (Deng et al., 2013).
Implicature in Chinese:
Agreement Study
 Train with English manual (Deng et al., 2013) and
several Chinese annotated examples.
 Annotate:
 (A). spans of the goodFor/badFor events
 (B). spans of the agents and objects of the events
 (C). polarities of the events: goodFor or badFor
 (D). writer’s sentiments toward the agents and
objects: positive, negative, neutral
 Evaluate by the same metrics as (Deng et al.,
2013):
 for (A) & (B): percentage of span both annotate
 for (C) & (D): kappa
Implicature in Chinese:
Agreement Study
 All the scores are good: trained by the English manual, the
annotators are able to detect similar implicature in Chinese.
 Scores of (A) and (D) are lower than those in the English
goodFor/badFor agreement study （Deng et al., 2014）.
overlap(a,b)
(A)
goodFor/badFor
span
(B) agent span
(B) object span
Anno 1&2
0.7929
0.9091
0.9091
Anno 1&3
0.7044
0.9524
1.0
(C)
goodFor/badFor
polarity
(D) sentiment
toward agent
(D) sentiment
toward object
Anno 1&2
0.9385
0.7830
0.7238
Anno 1&3
0.8966
0.5913
0.8478
kappa
Implicature in Chinese:
Agreement Study
 For annotating (D) writer’s sentiments, the main
disagreement comes from:
 Anno 1 annotated as positive or negative
 Anno 2 annotated as neutral
 We conduct a phase-II agreement study on 10
editorials from the English corpus (Deng et al., 2013).
 Three scores:
 I. agreement scores in Chinese by three annotators
 II. agreement scores in English by three annotators
 III. previous agreement scores (Deng et al., 2013)
 score I = score II; score I < score III; score II < score III
 They have a similar understanding of implicatures in the
two languages.
Implicature in Chinese:
Agreement Study
 For annotating (A) goodFor/badFor events, the
major disagreement comes from:
 Anno 1 marks a goodFor/badFor span
 Anno 2 doesn’t mark it because he thinks it violates
the syntax rules we specified in the English manual.
 Syntax rules are specified in the English manual to
guide the annotators to focus on clear cases of
goodFor/badFor events, e.g.
 The object should be the major semantic object.
 The goodFor/badFor polarity should be perceived
within the triple.
GoodFor/BadFor Cases Evoked
by Chinese Syntax
 The goodFor/badFor polarity should be
perceived within the triple.
 It will put the reform to die.
 In English: this is NOT annotated as a
goodFor/badFor event.
 put is the verb
 <it, put, reform>
 put X to die: badFor X
 put X to revive: goodFor X
GoodFor/BadFor Cases
Evoked by Chinese Syntax
 The goodFor/badFor polarity should be
perceived within the triple.
 It will put the reform to die.
 这将把改革置于死地。
 In Chinese: this can be represented as a clear
goodFor/badFor case
 “put” is not a verb in the Chinese sentence
 BA structure (Chao, 1968; Li and Thompson, 1989; Sybesma,
1992)
 subject, BA, object, verb
 it will BA kill the reform
Implicature in Chinese:
Conclusion
 Such syntax is commonly seen in Chinese.
 These goodFor/badFor events due to the Chinese
syntax are clear enough in Chinese.
 It will kill the reform.
 In order to fully study the Chinese goodFor/badFor,
the manual should be revised to provide guidance
to annotate such events.
 Overall, similar implicatures can be perceived in
English and in Chinese.
Outline
 Motivation
 Implicature in Chinese
 Inference in Chinese
 Graph Model for Sentiment Propagation (Deng and
Wiebe, 2014)
 Extracting Chinese GoodFor/BadFor
 Chinese GoodFor/BadFor Words
 Syntax of Chinese Agents/Objects
 Chinese Sentiment Analysis
 Conclusions
Graph Model (Deng and Wiebe, 2014)
agent
goodFor/badFor
object
agent/o
bject
goodFor
badFor
Encoding
Inference
Rules
Inference in Chinese:
Graph Model Performance
 We run an isolated evaluation of the graph
model itself (Deng and Wiebe, 2014).
 For a node, calculate how many times it is
propagated correctly given any neighbor node
being assigned with a correct sentiment label.
Dataset
# subgraph correctness
all subgraph
136
0.7058
multi-node
subgraph
61
0.8251
 The scores in Chinese are lower than those in
English (89% in (Deng and Wiebe, 2014)).
 Blocked Inference
Blocked Inference:
In Chinese and English
 …a misreading which estimated the law would
“reduce the amount of labor …
 <law, reduce, labor>
 The writer doesn’t believe <law, reduce, labor>.
 “misreading” believes so.
 The writer is negative toward “misreading”.
 For events which the writer doesn’t believe it is true,
the inference should be blocked.
 It is not in the writer’s belief space (Wiebe and Deng, 2014).
Inference in Chinese:
Conclusion
 Though there are cases where the inference rules
are blocked,
 The cases appear both in Chinese and in English.
 We didn’t find evidence showing that the blocked
inference only occurs in English.
 Besides the blocked inferences, the good
correctness scores provide evidence that the
inference rules also apply to Chinese.
Outline
 Motivation
 Implicature in Chinese
 Inference in Chinese
 Extract Chinese GoodFor/BadFor
 Chinese GoodFor/BadFor Words
 Syntax of Chinese Agents/Objects
 Chinese Sentiment Analysis
 English + Parallel Corpus?
 Conclusions
Chinese GoodFor/BadFor
Words
 Given we have an English goodFor/badFor lexicon (Choi
et al., 2014), is it applicable to derive a bilingual
goodFor/badFor lexicon from a parallel corpus?
 We manually find the parallel spans in English
corresponding to the annotated goodFor/badFor spans in
the Chinese.
 76.25% annotated Chinese goodFor/badFor spans have
parallel goodFor/badFor spans in English.
 For the other Chinese annotated goodFor/badFor spans,
there is no corresponding goodFor/badFor span in English,
due to:
 Chinese syntax;
 paraphrasing.
Chinese Agent/Object
 We use the Stanford dependency parser to extract the
agent/object in English (Deng et al., 2014).
 nsubj-(event, agent)
dobj-(event, object)
 Can we use the same dependency labels to extract
agent/object in Chinese?
 We choose the Chinese Stanford dependency parser.
 Some dependency labels exist both in Chinese and English.
 There are more nsubj and dobj in Chinese data than in English
data.
 Some labels are especially designed for Chinese (Chang et al.,
2009).
 19.57% in agents, 25.82% in objects.
 They are similar to some labels in English.
Chinese Sentiment Analysis
 Sentiment Lexicon:
 HowNet
 NTU Sentiment Dictionary (Ku and Chen, 2007)
 A sentiment lexicon from Tsinghua University (Li and
Sun, 2007)
 Bilingual and Multilingual Chinese Sentiment
Analysis Research
 Wan, 2008; Wan, 2009; Boyd-Graber and Resnik,
2010; Lu et al., 2011; etc.
 Chinese Sentiment Analysis Tools
 LingPipe http://alias-i.com/lingpipe/
 Semantria https://semantria.com/
Outline
 Motivation
 Implicature in Chinese
 Inference in Chinese
 Extracting Chinese GoodFor/BadFor
 Conclusions
Conclusions
 The implicatures that arise from explicit sentiment
toward goodFor/badFor events exist in Chinese
language and they are similar to those in English.
 The inference rules we developed for English apply
to Chinese.
 There are several cases where the inferences are
blocked and such cases exist both in Chinese and
English.
 It is promising to develop systems automatically
extracting Chinese goodFor/badFor events using
the existing methods for English and leveraging the
parallel corpus.
Questions ?
 Thank Fan Zhang and Changsheng Liu for
annotations.

Part of References:

Jordan Boyd-Graber and Philip Resnik. 2010. Holis- tic sentiment analysis across languages: Multilingual supervised
latent dirichlet allocation. In Proceedings of the 2010 Conference on Empirical Meth- ods in Natural Language
Processing.

Lingjia Deng and Janyce Wiebe. 2014. Sentiment propagation via implicature constraints. In Meeting of the European
Chapter of the Association for Computational Linguistics.

Lingjia Deng, Yoonjung Choi, and Janyce Wiebe. 2013. Benefactive/malefactive event and writer attitude annotation.
In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics.

Lun-Wei Ku and Hsin-Hsi Chen. 2007. Mining opinions from the web: Beyond relevance retrieval. Journal of the
American Society for Information Science and Technology.

Jun Li and Maosong Sun. 2007. Experimental study on sentiment classification of chinese review using machine learning
techniques. In Natural Language Processing and Knowledge Engineering, 2007.

Bin Lu, Chenhao Tan, Claire Cardie, and Benjamin K Tsou. 2011. Joint bilingual sentiment classification with unlabeled
parallel corpora. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human
Language Technologies.

Xiaojun Wan. 2008. Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis.
In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing.

Xiaojun Wan. 2009. Co-training for cross-lingual sentiment classification. In Proceedings of the Joint Conference of the
47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the
AFNLP.

Theresa Wilson and Janyce Wiebe. 2003. Annotating opinions in the world press. In Proceedings of the 4th ACL SIGdial
Workshop on Discourse and Dialogue.