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<Paper uid="P06-1134">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics Word Sense and Subjectivity</Title>
  <Section position="4" start_page="0" end_page="1065" type="intro">
    <SectionTitle>
2 Background
</SectionTitle>
    <Paragraph position="0"> Subjective expressions are words and phrases being used to express opinions, emotions, evaluations, speculations, etc. (Wiebe et al., 2005). A general covering term for such states is private state, &amp;quot;a state that is not open to objective obser- null vation or verification&amp;quot; (Quirk et al., 1985).1 There are three main types of subjective expressions:2 (1) references to private states:  His alarm grew.</Paragraph>
    <Paragraph position="1"> He absorbed the information quickly.</Paragraph>
    <Paragraph position="2"> He was boiling with anger.</Paragraph>
    <Paragraph position="3"> (2) references to speech (or writing) events expressing private states: UCC/Disciples leaders roundly condemned the Iranian President's verbal assault on Israel.</Paragraph>
    <Paragraph position="4"> The editors of the left-leaning paper attacked the new House Speaker.</Paragraph>
    <Paragraph position="5"> (3) expressive subjective elements: He would be quite a catch.</Paragraph>
    <Paragraph position="6"> What's the catch? That doctor is a quack.</Paragraph>
    <Paragraph position="7"> Work on automatic subjectivity analysis falls into three main areas. The first is identifying words and phrases that are associated with subjectivity, for example, that think is associated with private states and that beautiful is associated with positive sentiments (e.g., (Hatzivassiloglou and McKeown, 1997; Wiebe, 2000; Kamps and Marx, 2002; Turney, 2002; Esuli and Sebastiani, 2005)). Such judgments are made for words. In contrast, our end task (in Section 4) is to assign subjectivity labels to word senses.</Paragraph>
    <Paragraph position="8"> The second is subjectivity classification of sentences, clauses, phrases, or word instances in the context of a particular text or conversation, either subjective/objective classifications or positive/negative sentiment classifications (e.g.,(Riloff and Wiebe, 2003; Yu and Hatzivassiloglou, 2003; Dave et al., 2003; Hu and Liu, 2004)).</Paragraph>
    <Paragraph position="9"> The third exploits automatic subjectivity analysis in applications such as review classification (e.g., (Turney, 2002; Pang and Lee, 2004)), mining texts for product reviews (e.g., (Yi et al., 2003; Hu and Liu, 2004; Popescu and Etzioni, 2005)), summarization (e.g., (Kim and Hovy, 2004)), information extraction (e.g., (Riloff et al., 2005)),  but may help the reader appreciate the examples given below. and question answering (e.g., (Yu and Hatzivassiloglou, 2003; Stoyanov et al., 2005)). Most manual subjectivity annotation research has focused on annotating words, out of context (e.g., (Heise, 2001)), or sentences and phrases in the context of a text or conversation (e.g., (Wiebe et al., 2005)). The new annotations in this paper are instead targeting the annotation of word senses.</Paragraph>
  </Section>
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