Josef Ruppenhofer, Swapna Somasundaran, Janyce Wiebe University of Pittsburgh Finding the sources and targets of subjective expressions

2 What is Subjectivity? The linguistic expression of somebody’s opinions, sentiments, emotions, evaluations, beliefs, speculations (private states) This pleased the mainly female audience. Source: the person who experiences a private state Target: what the private state is about or directed towards

3 Motivation Sentiment analysis is a fast-growing field with many applications (e.g. Question Answering, Product review mining, Information Extraction) In many kinds of texts we find opinions attributed to several different sources, and/or opinions about multiple targets

4 Opinion Question Answering Q: What is the international reaction to the reelection of Robert Mugabe as President of Zimbabwe? A: African observers generally approved of his victory while Western Governments strongly denounced it.

5 Product review mining The computer is very good and very easy to use. It has a built in camera, bluetooth; the all singing and dancing machine. Love it. The only glitch is the scrolling pad is not as smooth as my last Toshiba notebook. One other thing is that Vista is a nightmare...

6 Motivation Sentiment analysis is a fast-growing field with many applications (e.g. Question Answering, Product review mining, Information Extraction) In many kinds of texts we find opinions attributed to several different sources, and/or opinions about multiple targets Challenge is to associate sources, opinions, and targets correctly

7 Roadmap Here we discuss some of the challenges that an automatic system needs to be able to deal with We take the use of Automatic Semantic Role Labeling (ASRL) systems as our starting point Based on our work in corpus annotation, we show that we need additional capabilities beyond ASRL

8 Automatic Semantic Role Labeling

9 Semantic roles This pleased the mainly female audience. PropBankFrameNet Arg0Experiencer Arg1Content PropBankFrameNet Arg1Experiencer Arg0Stimulus We fear an early death much more.

10 Mapping opinion roles to semantic roles This pleased the mainly female audience. Opinion rolesPropBankFrameNet SourceArg0Experiencer TargetArg1Content Opinion rolesPropBankFrameNet SourceArg1Experiencer TargetArg0Stimulus We fear an early death much more.

11 Annotation scheme

12 Private states (Quirk et al. 1985) - States such as emotions, evaluations, speculations, etc. - States that are not open to objective observation or verification. - States that involve a particular person’s point of view - Private states involve sources holding attitudes, typically towards targets.

13 Ways of evoking private states Explicit mentions: He was boiling with anger. Speaking events expressing private states: The paper’s editors attacked the new House Speaker. Expressive subjective elements (Banfield 1982): That doctor is a quack. Objective speech events: “The bus leaves at 4”, Bill said. DSEs ESEs

14 Nesting of private states ``The US fears a spill-over,” said Xirao- Nima.

15 Nesting of private states ``The US fears a spill-over,” said Xirao- Nima. The US fears a spill-over “ “ said Xirao-Nima

16 Challenges beyond role labeling SourcesTargets Attribution Referent Identification Inferences Arguing Attitudes

17 Attribution

18 Attribution Expressive subjective elements (ESEs) don’t have a semantic role for their source: Senior Mike Sheehy said, “It was a blast ”.

19 Attribution The source for an ESE is not always at the same level. Compare: Senior Mike Sheehy said, “It was a blast”. She loves that idiot.

20 Senior Mike Sheey said, “It was a blast” She loves that idiot.

21 Attribution Some expressions function both as ESEs and as DSEs:

22 Attribution Some expressions function both as ESEs and as DSEs: It is a shame that there is no jury that can mete out justice for a city he has slandered for far too long. sourcetarget DSEHecity ESE he

23 Attribution Attribution and content of a private state may be presented separately:

24 Attribution Attribution and content of a private state may be presented separately: Chris Moyles is a brilliant broadcaster, the saviour of Radio 1, a comedian, a best-selling author, and, in fact, a genius. Or so he says.

25 Attribution Chris Moyles is a brilliant broadcaster, the saviour of Radio 1, a comedian, a best-selling author, and, in fact, a genius.

26 Attribution Chris Moyles is a brilliant broadcaster, the saviour of Radio 1, a comedian, a best-selling author, and, in fact, a genius. Or so he says

27 Attribution An attribution may apply to several utterances without being explicitly signaled each time. And I went ahead and mailed it in thinking uh I won’t get the scholarship. Who cares? I don’t, just so I can work in the school and I’ll be happy. But one day I came in and I looked at my mail and I was accepted.

28 Reference Identification

29 Reference Identification Overt referents: You think about it and then let me know. Some people say the Steelers are contenders but I’m not convinced of it.

30 Reference Identification Zero referents: Source: Think about it and then let me know. Target: Some people say the Steelers are contenders but I’m not convinced.

31 Reference Identification Exophora: referents are present only in the physical context Sorry. Oopsy-daisy.

32 Inferences

33 Inferences For some events about which opinions are expressed, we can infer additional attitudes towards affected or causing participants:

34 Inferences For some events about which opinions are expressed, we can infer additional attitudes towards affected or causing participants: I think people are happy because Chavez has fallen.

35 Inferences For some events about which opinions are expressed, we can infer additional attitudes towards affected or causing participants: I think people are happy because Chavez has fallen. I think people are happy because Chavez has fallen.

36 Inferences The targets occurring in a discourse are often interrelated such that opinions about local targets contribute to the overall assessment of a global target. [The computer] is very good and very easy to use. [It] has a built in camera, bluetooth; the all singing and dancing machine. Love [it]. The only glitch is [the scrolling pad] is not as smooth as my last Toshiba notebook. One other thing is that [Vista] is a nightmare...

37 Targets of arguing

38 Arguing attitudes 1. What is the case or not From this it follows that mechanisation is not economic unless it can produce higher yields of crops than these older methods. 2. What should be done or not We strongly recommend that all Firefox users upgrade to this latest release.

39 Targets of arguing Interpretation of arguments made by causal and conditional constructions is very context- dependent. Your presentation will be better [if you put this on the first slide] You will want to vote YES [if you want to keep the cost of government in Lewiston low]

40 Targets of arguing Interpretation of arguments made by causal and conditional constructions is very context- dependent. Hypothetical: Your presentation will be better [if you put this on the first slide] Implicit assertion You will want to vote YES [if you want to keep the cost of government in Lewiston low]

41 Targets of arguing Easy finder with the a whistle function or something, or rechargeable station [because it’s a pain when you run out of batteries]. If you’re not a good cook, then taking your girlfriend out to an expensive restaurant might be the next best romantic date idea.... [You’ll feel good because] you’ve made her happy with a romantic date.

42 Targets of arguing The prototypical targets that we annotate are entities. For arguing, we could also annotate the entities that arguments are about. However, we also recognize that the logical targets of arguing are propositions.

43 Targets of arguing Clinton should be the presidential candidate. Clinton should be the running mate. The prototypical targets that we annotate are entities. For arguing, we could also annotate the entities that arguments are about. However, we also recognize that the logical targets of arguing are propositions.

44 Targets of arguing Clinton should be the presidential candidate. Clinton should be the running mate. The prototypical targets that we annotate are entities. For arguing, we could also annotate the entities that arguments are about. However, we also recognize that the logical targets of arguing are propositions.

45 Targets of arguing Clinton should be the presidential candidate. Clinton should be the running mate. The prototypical targets that we annotate are entities. For arguing, we could also annotate the entities that arguments are about. However, we also recognize that the logical targets of arguing are propositions.

46 Targets of arguing Clinton should be the presidential candidate. Clinton should be the running mate. The prototypical targets that we annotate are entities. For arguing, we could also annotate the entities that arguments are about. However, we also recognize that the logical targets of arguing are propositions.

47 Conclusion Semantic role labeling is needed for finding sources and targets But we also need ways of establishing levels of attribution capabilities for dealing with zero references lexical information to support inferences deal with the full variety of attitudes and their sources and targets

Thanks!

49 References: Annotation scheme Banfield, Ann Unspeakable Sentences: Narration and Representation in the Language of Fiction. Routledge & Kegan Paul, Boston. Quirk Randolph, Greenbaum Sidney, Leech Geoffrey, and Svartvik Jan A Comprehensive Grammar of the English Language. Longman, New York, NY. Janyce Wiebe M Tracking point of view in narrative. Computational Linguistics 20 (2):

50 References: Annotation scheme Janyce Wiebe, Theresa Wilson, and Claire Cardie Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, volume 39, issue 2-3, pp

51 References: Role Labeling Penn Discourse Treebank annotation-manual.pdf annotation-manual.pdf PropBank html html FrameNet

52 References: Role Labeling Y. Choi, E. Breck, and C. Cardie Joint Extraction of Entities and Relations for Opinion Recognition. In Proc. of EMNLP S. Kim and E. Hovy Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text. In ACL Workshop on Sentiment and Subjectivity in Text.

53 References: Belief spaces Dyer, Michael G In-Depth Understanding: A Computer Model of Integrated Processing for Narrative Comprehension. MIT Press, Cambridge, MA. Fauconnier, Gilles Mental Spaces: Aspects of Meaning Construction in Natural Language. MIT Press, Cambridge, MA.

54 References: Belief spaces Rapaport, William J Logical Foundations for Belief Representation. Cognitive Science. Wilks, Yorick and Bien, Janusz. Beliefs, Points of View, and Multiple Environments. Cognitive Science 7:

55 References: Literary theory Chatman, Seymour Story and Discourse: Narrative Structure in Fiction and Film. Cornell University Press, Ithaca, NY. Cohn, Dorrit Transparent Minds: Narrative Modes for Representing Consciousness in Fiction Princeton University Press, Princeton, NJ. Dolezel, Lubomir Narrative Modes in Czech Literature. University of Toronto Press, Toronto, Canada.

56 References: Literary theory Hamburger Käte M.J. Rose, Trans., The Logic of Literature. Indiana University Press, Bloomington, Indiana. Kuroda, S.-Y Reflections on the Foundations of Narrative Theory--From a Linguistic Point of View. In: van Dijk, T.A., Ed., Pragmatics of Language and Literature, North Holland, Amsterdam. Uspensky, Boris A Poetics of Composition. University of California Press, Berkeley, CA.

57 References: Discourse Allen, James F. and Perrault, C. Raymond Analyzing Intention in Utterances. Artificial Intelligence 15: Fillmore, Charles Pragmatics and the Description of Discourse. In: Fillmore, Charles, Lakoff, George, and Lakoff, Robin, Eds., Berkeley Studies in Syntax and Semantics I. University of California Dept. of Linguistics and Institute of Human Learning, Berkeley, CA: V1-V21.

Extra slides

59 Nesting of private states ``The US fears a spill-over,” said Xirao- Nima. LevelPrivate state spansource 0<> 1saidXirao-Nima 2fearsthe US

60 Nesting of private states ``The US fears a spill-over,” said Xirao- Nima. LevelPrivate state spansource 0<> 1saidXirao-Nima 2fearsthe US

61 Nesting of private states ``The US fears a spill-over,” said Xirao- Nima. LevelPrivate state spansource 0<> 1saidXirao-Nima 2fearsthe US