In this paper, we describe a case study of a sentence-level categorization in which tagging instructions are developed and used by four judges to classify clauses from the Wall Street Journal as either subjective or objective. Agreement among the four judges is analyzed, and, based on that analysis

was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness. 1 Introduction Text classification is the automated

time. These patterns have positive practical implications for automation. Keywords: Subjectivity, manual tagging, natural language processing, uncertainty, epistemic comments, evidentials, hedges, certainty expressions; point of view, annotating opinions. 62 CERTAINTY IDENTIFICATION IN TEXTS

This chapter presents a theoretical framework and preliminary results for manual categorization of explicit certainty information in 32 English newspaper articles. Our contribution is in a proposed categorization model and analytical framework for certainty identification. Certainty is presented

The pervasiveness of ambiguity is a major issue in the parsing of natural languages. In this respect, morphosyntactic tagging can be seen as a first step in the process of resolving

Many corpus-based natural language processing systems rely on text corpora that have been manually annotated with syntactic or semantic tags. In particular, all previous dictionary construction systems for information extraction have used an annotated training corpus or some form of annotated input

and to manually create new extraction rules or hand-tag new training examples. This manual labor scales linearly with the number of target relations. This paper introduces Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set

This paper presents a project whose main goal is to construct a corpus of clinical text manually annotated for part-of-speech information. We describe and discuss the process of training three domain experts to perform linguistic annotation. We list some of the challenges as well as encouraging

In this paper we describe an unsupervised learning algorithm for automatically training a rule-based part of speech tagger without using a manually tagged corpus. We compare this algorithm to the Baum-Welch algorithm, used for unsupervised training of stochastic taggers. Next, we show a method

1.1.1 Manual Tagging.......................... 7