Using Hedge Detection to Improve Committed Belief Tagging
We describe a novel method for identifying hedge terms using a set of manually constructed rules. We present experiments adding hedge features to a committed belief system to improve classification. We compare performance of this system (a) without hedging features, (b) with dictionary-based features, and (c) with rule-based features. We find that using hedge features improves performance of the committed belief system, particularly in identifying instances of non-committed belief and reported belief.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationSentence ClassificationSimilar Papers 제목 키워드 기반
Committed Belief Tagging on the Factbank and LU Corpora: A Comparative Study
Detecting Level of Belief in Chinese and Spanish
There has been extensive work on detecting the level of committed belief (also known as {``}factuality{''}) that an author is expressing towards the propositions in his or her utterances. Previous work on English has rev…
''You should probably read this'': Hedge Detection in Text
Humans express ideas, beliefs, and statements through language. The manner of expression can carry information indicating the author's degree of confidence in their statement. Understanding the certainty level of a claim…
A Quantitative and Qualitative Analysis of Schizophrenia Language
Schizophrenia is one of the most disabling mental health conditions to live with. Approximately one percent of the population has schizophrenia which makes it fairly common, and it affects many people and their families.…
SpecificityIntention as Commitment toward Time
In this paper we address the interplay among intention, time, and belief in dynamic environments. The first contribution is a logic for reasoning about intention, time and belief, in which assumptions of intentions are r…