We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier using the subjective nouns, discourse features, and subjectivity clues identified in prior research. The bootstrapping algorithms learned over 1000 subjective nouns, and the subjectivity classifier performed well, achieving 77% recall with 81% precision.

This paper presents this algorithm, gives demonstrations of an implemented system, and describes the results of some preliminary empirical studies, which lend support to the algorithm

There has been a recent swell of interest in the automatic identification and extraction of opinions and emotions in text. In this paper, we present the first experimental results classifying the intensity of opinions and other types of subjectivity and classifying the subjectivity of deeply nested clauses. We use a wide range of features, including new syntactic features developed for opinion recognition. We vary the learning algorithm and the feature organization to explore the effect this has on the classification task. In 10-fold cross-validation experiments using support vector re-gression, we achieve improvements in mean-squared error over baseline ranging from 49 % to 51%. Using boosting, we achieve improvements in accuracy ranging from 23 % to 96%.

We present a computational analysis of de re, de dicto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector-Neri Casta~neda, namely, that the simple rule `(A knows that P ) implies P ' apparently does not hold if P contains a quasi-indexical. We present a single rule, in the context of a knowledge-representation and reasoning system, that holds for all P , including those containing quasi-indexicals. In so doing, we explore the difference between reasoning in a public communication language and in a knowledge-representation language, we demonstrate the importance of representing proper names explicitly, and we provide support for the necessity of considering sentences in the context of extended discourse (for example, written narrative) in order to fully capture certain features of their semantics. (This document is SUNY Buffalo Department of Computer Science Technical Report No. 95-49B, as well as SUNY Buffalo Center for Cognitive Science Tec...

This project continues our interdisciplinary research into computational and cognitive aspects of narrative comprehension. Our ultimate goal is the development of a computational theory of how humans understand narrative texts. The theory will be informed by joint research from the viewpoints of linguistics, cognitive psychology, the study of language acquisition, literary theory, geography, philosophy, and artificial intelligence. The linguists, literary theorists, and geographers in our group are developing theories of narrative language and spatial understanding that are being tested by the cognitive psychologists and language researchers in our group, and a computational model of a reader of narrative text is being developed by the AI researchers, based in part on these theories and results and in part on research on knowledge representation and reasoning. This proposal describes the knowledge-representation and natural-language-processing issues involved in the computational implementation of the theory; discusses a contrast between communicative and narrative uses of language and of the relation of the narrative text to the story world it describes; investigates linguistic, literary, and hermeneutic dimensions of our research; presents a computational investigation of subjective sentences and reference in narrative; studies children’s acquisition of the ability to take third-person perspective in their own storytelling; describes the psychological validation of various linguistic devices; and examines how readers develop an understanding of the geographical space of a story. This report is a longer

The propositional content of a reference is the proposition attributing to the referent the properties that correspond to the nouns and modifiers in the reference (for example, the propositional content of `Mary' is that the referent is named `Mary'). During language comprehension, the hearer or reader must determine the set of beliefs with respect to which the propositional content of a reference is to be understood. In the prototypical case, this set consists of the propositions that she believes that the speaker or writer believes that she and the speaker or writer mutually believe. This paper identifies two contexts in which the propositional content of a specific reference is not understood with respect to this set--- subjective and objective sentences in third-person fictional narrative text---and identifies some implications of this for understanding specific references in these contexts. 1 Introduction Specific references are references to particular entities, for example, `a...

This paper discusses the theoretical background and some of the results of an interdisciplinary, cognitive-science research project on the comprehension of narrative text. The unifying theme of our work has been the notion of a deictic center: a mental model of spatial, temporal, and character information contributed by the reader of the narrative and used by the reader in understanding the narrative. We examine the deictic center in the light of our investigations from the viewpoints of linguistics, cognitive psychology, individual differences (language pathology), literary theory of narrative, and artificial intelligence.

This paper addresses discourse structure from the perspective of understanding. It would perhaps help

Current research in automatic subjectivity analysis deals with various kinds of subjective statements involving human attitudes and emotions. While all of them are related to subjectivity, these statements usually touch on multiple dimensions such as non-objectivity1, uncertainty, vagueness, non-objective measurability, imprecision, and ambiguity, which are inherently different. This paper discusses the differences and relations of six dimensions of subjectivity. Conceptual and linguistic characteristics of each dimension will be demonstrated under different contexts. 1

this paper, Orilia explores "[t]he design of artificial agents with . . . sophisticated representational and deductive capacities", such as "a solution to . . . intensional context problems" and proposes "an alternative to [AI researcher Kurt] Konolige 's [1986] modal first-order language . . . [Orilia's being] based on type-free property theory" (Orilia 1994: 163). He cites several of Castaneda's papers and books, primarily on quasi-indicators and guise theory (as well as my own 1986 Cognitive Science paper).