Pranav Anand

Dayta AI

Other people with similar names: Pranav A (UC Santa Cruz)


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Linguistic Reflexes of Well-Being and Happiness in Echo
Jiaqi Wu | Marilyn Walker | Pranav Anand | Steve Whittaker
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Different theories posit different sources for feelings of well-being and happiness. Appraisal theory grounds our emotional responses in our goals and desires and their fulfillment, or lack of fulfillment. Self-Determination theory posits that the basis for well-being rests on our assessments of our competence, autonomy and social connection. And surveys that measure happiness empirically note that people require their basic needs to be met for food and shelter, but beyond that tend to be happiest when socializing, eating or having sex. We analyze a corpus of private micro-blogs from a well-being application called Echo, where users label each written post about daily events with a happiness score between 1 and 9. Our goal is to ground the linguistic descriptions of events that users experience in theories of well-being and happiness, and then examine the extent to which different theoretical accounts can explain the variance in the happiness scores. We show that recurrent event types, such as obligation and incompetence, which affect people’s feelings of well-being are not captured in current lexical or semantic resources.

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Modelling Protagonist Goals and Desires in First-Person Narrative
Elahe Rahimtoroghi | Jiaqi Wu | Ruimin Wang | Pranav Anand | Marilyn Walker
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Many genres of natural language text are narratively structured, a testament to our predilection for organizing our experiences as narratives. There is broad consensus that understanding a narrative requires identifying and tracking the goals and desires of the characters and their narrative outcomes. However, to date, there has been limited work on computational models for this problem. We introduce a new dataset, DesireDB, which includes gold-standard labels for identifying statements of desire, textual evidence for desire fulfillment, and annotations for whether the stated desire is fulfilled given the evidence in the narrative context. We report experiments on tracking desire fulfillment using different methods, and show that LSTM Skip-Thought model achieves F-measure of 0.7 on our corpus.

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Learning Lexico-Functional Patterns for First-Person Affect
Lena Reed | Jiaqi Wu | Shereen Oraby | Pranav Anand | Marilyn Walker
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Informal first-person narratives are a unique resource for computational models of everyday events and people’s affective reactions to them. People blogging about their day tend not to explicitly say I am happy. Instead they describe situations from which other humans can readily infer their affective reactions. However current sentiment dictionaries are missing much of the information needed to make similar inferences. We build on recent work that models affect in terms of lexical predicate functions and affect on the predicate’s arguments. We present a method to learn proxies for these functions from first-person narratives. We construct a novel fine-grained test set, and show that the patterns we learn improve our ability to predict first-person affective reactions to everyday events, from a Stanford sentiment baseline of .67F to .75F.

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Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion
Stephanie Lukin | Pranav Anand | Marilyn Walker | Steve Whittaker
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments.


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Internet Argument Corpus 2.0: An SQL schema for Dialogic Social Media and the Corpora to go with it
Rob Abbott | Brian Ecker | Pranav Anand | Marilyn Walker
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Large scale corpora have benefited many areas of research in natural language processing, but until recently, resources for dialogue have lagged behind. Now, with the emergence of large scale social media websites incorporating a threaded dialogue structure, content feedback, and self-annotation (such as stance labeling), there are valuable new corpora available to researchers. In previous work, we released the INTERNET ARGUMENT CORPUS, one of the first larger scale resources available for opinion sharing dialogue. We now release the INTERNET ARGUMENT CORPUS 2.0 (IAC 2.0) in the hope that others will find it as useful as we have. The IAC 2.0 provides more data than IAC 1.0 and organizes it using an extensible, repurposable SQL schema. The database structure in conjunction with the associated code facilitates querying from and combining multiple dialogically structured data sources. The IAC 2.0 schema provides support for forum posts, quotations, markup (bold, italic, etc), and various annotations, including Stanford CoreNLP annotations. We demonstrate the generalizablity of the schema by providing code to import the ConVote corpus.

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Antecedent Selection for Sluicing: Structure and Content
Pranav Anand | Daniel Hardt
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


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Using Summarization to Discover Argument Facets in Online Idealogical Dialog
Amita Misra | Pranav Anand | Jean E. Fox Tree | Marilyn Walker
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Annotating the Implicit Content of Sluices
Pranav Anand | Jim McCloskey
Proceedings of the 9th Linguistic Annotation Workshop


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A Corpus for Research on Deliberation and Debate
Marilyn Walker | Jean Fox Tree | Pranav Anand | Rob Abbott | Joseph King
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Deliberative, argumentative discourse is an important component of opinion formation, belief revision, and knowledge discovery; it is a cornerstone of modern civil society. Argumentation is productively studied in branches ranging from theoretical artificial intelligence to political rhetoric, but empirical analysis has suffered from a lack of freely available, unscripted argumentative dialogs. This paper presents the Internet Argument Corpus (IAC), a set of 390,704 posts in 11,800 discussions extracted from the online debate site A 2866 thread/130,206 post extract of the corpus has been manually sided for topic of discussion, and subsets of this topic-labeled extract have been annotated for several dialogic and argumentative markers: degrees of agreement with a previous post, cordiality, audience-direction, combativeness, assertiveness, emotionality of argumentation, and sarcasm. As an application of this resource, the paper closes with a discussion of the relationship between discourse marker pragmatics, agreement, emotionality, and sarcasm in the IAC corpus.

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POLITICAL-ADS: An annotated corpus for modeling event-level evaluativity
Kevin Reschke | Pranav Anand
Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis

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Annotating the Focus of Negation in terms of Questions Under Discussion
Pranav Anand | Craig Martell
Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics

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Stance Classification using Dialogic Properties of Persuasion
Marilyn Walker | Pranav Anand | Rob Abbott | Ricky Grant
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Extracting Contextual Evaluativity
Kevin Reschke | Pranav Anand
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

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How can you say such things?!?: Recognizing Disagreement in Informal Political Argument
Rob Abbott | Marilyn Walker | Pranav Anand | Jean E. Fox Tree | Robeson Bowmani | Joseph King
Proceedings of the Workshop on Language in Social Media (LSM 2011)

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Cats Rule and Dogs Drool!: Classifying Stance in Online Debate
Pranav Anand | Marilyn Walker | Rob Abbott | Jean E. Fox Tree | Robeson Bowmani | Michael Minor
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)


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Looking Under the Hood: Tools for Diagnosing Your Question Answering Engine
Eric Breck | Marc Light | Gideon Mann | Ellen Riloff | Brianne Brown | Pranav Anand
Proceedings of the ACL 2001 Workshop on Open-Domain Question Answering