Subjectivity tagging is distinguishing sentences used to present opinions and evaluations from sentences used to objectively present factual information. There are numerous applications for which subjectivity tagging is relevant, including information extraction and information retrieval. This paper identifies strong clues of subjectivity using the results of a method for clustering words according to distributional similarity (Lin 1998), seeded by a small amount of detailed manual annotation. These features are then further refined with the addition of lexical semantic features of adjectives, specifically polarity and gradability (Hatzivassiloglou & McKeown 1997), which can be automatically learned from corpora. In 10-fold cross validation experiments, features based on both similarity clusters and the lexical semantic features are shown to have higher precision than features based on each alone.

Subjectivity in natural language refers to aspects of language used to express opinions and evaluations (Banfield, 1982

While much recent progress has been made in research on fact-based question answering, our work aims to extend question-answering research in a different direction --- to handle multi-perspective question-answering tasks, i.e. question-answering tasks that require an ability to find and organize opinions in text. In particular, this paper proposes an approach to multi-perspective question answering that views the task as one of opinion-oriented information extraction. We first describe an annotation scheme developed for the low-level representation of opinions, and note the results of interannotator agreement studies using the opinion-based annotation framework. Next, we propose the use of opinion-oriented "scenario templates" to act as a summary representation of the opinions expressed in a document, a set of documents, or an arbitrary text segment. Finally, we outline an approach for the automatic construction of opinion-based summary representations and describe how they might be used to support a variety of multi-perspective question answering tasks.

This paper presents a corpus study

In this paper we present a detailed scheme for annotating expressions of opinions, beliefs, emotions, sentiment and speculation (private states) in the news and other discourse. We explore inter-annotator agreement for individual private state expressions, and show that these low-level annotations are useful for producing higher-level subjective sentence annotations.

Immediate contexts and reported speech *