• Corpus ID: 8234803

Probabilistic Classifiers for Tracking Point of View

  title={Probabilistic Classifiers for Tracking Point of View},
  author={Janyce Wiebe and Rebecca F. Bruce},
This paper describes work in developing probabilistic classifiers for a discourse segmentation problem that involves segmentation, reference resolution, and belief. Specifically, the problem is to segment a text into blocks such that all subjective sentences in a block are from the point of view of the same agent, and to identify noun phrases that refer to that agent. In our method for developing classifiers (Bruce & Wiebe 1994ab), rather than making assumptions about which variables to use and… 

Extracting and Attributing Quotes in Text and Assessing them as Opinions

This thesis sets out to detect instances of reported speech, attribute them to their speaker, and to identify which instances provide evidence of an opinion, and develops new methods and features for quote attribution which achieve state-of-the-art accuracy on the authors' corpus and strong results on the others.

Sentiment analysis based on appraisal theory and functional local grammars

The IIT sentiment corpus, intended to present an alternative to both of these assumptions that have pervaded structured sentiment analysis research, consists of blog posts annotated with appraisal expressions to enable the evaluation of how well sentiment analysis systems find individual appraisal expressions.

Feature weighting approaches in sentiment analysis of short text

The experiments on datasets in Chinese and Japanese show a comparable level of performance of the proposed scheme with the results obtained on the English datasets without any use of natural language speci c techniques.

Opinion Mining and Sentiment Analysis

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.

of the EACL 2009 Workshop on Computational Linguistic Aspects of Grammatical Inference CLAGI 2009 30 March – 2009

This talk will discuss initial work in treating foreign language as a code for English, where it is assumed the code to involve both word substitutions and word transpositions, and quantitatively estimate the value of non-parallel data, versus parallel data, in terms of end-to-end accuracy of trained translation systems.

Visualizing Narrative Threads in a Large Collection of Documents

This thesis develops a novel technique to generate and visualize narrative threads from a large collection of documents based on properties derived from document clustering and sentiment analysis, then presents the results as a line graph that visualizes the interaction between entities in a thread based on their sentiment.

Evolution of Opinion Mining

The purpose of this paper is to walk through the timeline of opinion mining by identifying the changing direction of scholarly studies in this area by including some of the significantly contributing papers in this field from the year 1979 to 2011.

Sentiment analysis and real-time microblog search

This thesis proposes a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios, and provides an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts.

Interactive Exploration of Consensus in Climate Science

An approach to automatically classify stance on anthropogenic (human-induced) global warming in climate science with the aim to investigate the development of the consensus after 2011, and achieves a substantial improvement over the baseline.

An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief

This chapter explores applications of sentiment analysis and demonstrates how sentiment mining in social media can be exploited to determine how local crowds react during a disaster, and how such information can be used to improve disaster management.



Empirical Studies on the Disambiguation of Cue Phrases

This paper reports results of empirical studies on discourse and sentential uses of cue phrases, in which both text-based and prosodic features were examined for disambiguating power.

Corpus-Driven Knowledge Acquisition for Discourse Analysis

This paper finds that it is nevertheless possible to use ML algorithms in order to capture knowledge that is only implicitly present in a representative text corpus, and addresses issues traditionally associated with discourse analysis and intersentential inference generation.

Word-Sense Disambiguation Using Decomposable Models

This paper describes a method for formulating probabilistic models that use multiple contextual features for word-sense disambiguation, without requiring untested assumptions regarding the form of the model.

Recognizing subjective sentences: a computational investigation of narrative text

Part of understanding third-person fictional narrative text is determining for each sentence whether it takes some character's psychological point of view (is subjective) and, if it does, identifying

Tracking Point of View in Narrative

  • J. Wiebe
  • Philosophy
    Comput. Linguistics
  • 1994
This paper presents an algorithm to develop an algorithm that tracks point of view on the basis of the regularities found in naturally occurring narrative, and describes the results of some preliminary empirical studies, which lend support to the algorithm.

Classifying Cue Phrases in Text and Speech Using Machine Learning

This paper explores the use of machine learning for classifying cue phrases as discourse or sentential, and two machine learning programs are used to induce classification rules from sets of pre-classified cue phrases and their features.

Attention, Intentions, and the Structure of Discourse

A new theory of discourse structure that stresses the role of purpose and processing in discourse is explored and various properties of discourse are described, and explanations for the behavior of cue phrases, referring expressions, and interruptions are explored.

Probabilistic reasoning in intelligent systems - networks of plausible inference

  • J. Pearl
  • Computer Science
    Morgan Kaufmann series in representation and reasoning
  • 1989
The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.

Mental Spaces: Aspects of Meaning Construction in Natural Language

A finding that challenges several traditional and widespread views on meaning and natural language, with far-reaching implications: adequate theories of truth and reference cannot bypass the cognitive space-construction process, and standard linguistic arguments for hidden structural levels are invalidated.

Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

  • S. GemanD. Geman
  • Physics
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 1984
The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.