Generating More-Positive and More-Negative Text

We present experiments on modifying the semantic orientation of the near-synonyms in a text. We analyze a text into an interlingual representation and a set of attitudinal nuances, with particular focus on its near-synonyms. Then we use our text generator to produce a text with the same meaning but changed semantic orientation (more positive or more negative) by replacing, wherever possible, words with nearsynonyms that differ in their expressed attitude. Near-synonyms and attitudinal nuances The choice of a word from among a set of near-synonyms that share the same core meaning but vary in their connotations is one of the ways in which a writer controls the nuances of a text. In many cases, the nuances that differentiate near-synonyms relate to expressed attitude and affect. For example, if a writer wants to express a more-favorable view of the appearance of a relatively narrow person, he or she can use the words slim or slender; if the writer wants to express a less-favorable view, the word skinny is available. This level of attitude expression is distinct from that of the opinions expressed in the text as a whole, and may in fact contradict it. In particular, euphemism is the expression of a critical or unpleasant message in relatively positive or favorable terms; dysphemism is the converse (Allan & Burridge 1991). Nonetheless, the term semantic orientation has been used to describe attitudes at both levels. Any natural language understanding or generation system must be sensitive to this kind of nuance in text if it is to do its work well. A machine translation system, especially, must recognize such nuances in the source text and preserve them in the target text. If the source is, say, polite, angry, or obsequious, then the translation must be too. Nonetheless, in this paper we look at changing the nuances of a text rather than preserving them. We see this primarily as an exercise in the control of nuances in text, and hence a test of a natural language generation system, rather than as a useful application that is an end in itself. That is, any system that purports to accurately preserve nuances should be equally able to change nuances as desired, and Copyright c © 2004, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. render its input in a variety of ways. However, it is possible that a system that can change the nuances of a text could sometimes be helpful — for example, in the customization of texts for users. When generating text that expresses a strong opinion, a negative or positive tone may reflect the speaker’s point of view. In this paper, we propose to automatically transform the low-level semantic orientation of a text by choosing near-synonyms accordingly. In our previous work (Inkpen 2003; Inkpen & Hirst 2001) we automatically acquired a lexical knowledge-base of nearsynonym differences (LKB of NS) from the explanatory text of a special dictionary of synonym discrimination, Choose the Right Word (hereafter CTRW) (Hayakawa 1994). The main types of distinctions (nuances) that we extracted were: stylistic (for example, inebriated is more formal than drunk), attitudinal (for example, skinny is more pejorative than slim), and denotational (for example, blunder implies accident and ignorance, while error does not). The computational model we use for representing the meaning of near-synonyms was initially proposed by Edmonds and Hirst (2002). We enriched the initial LKB of NS with additional information extracted from other sources. Knowledge about the collocational behavior of the near-synonyms was acquired from free text (Inkpen & Hirst 2002). More knowledge about distinctions between near-synonyms was acquired from machine-readable dictionaries: attitudinal distinctions from the General Inquirer, and denotational distinctions from word definitions in the Macquarie Dictionary. These distinctions were merged with the initial LKB of NS, and inconsistencies were resolved. Our final LKB of NS has 904 clusters containing a total of 5,425 nearsynonyms. The General Inquirer (Stone et al. 1966) is particularly important in this facet of our work. It is a computational lexicon compiled from several sources, including the Harvard IV-4 dictionary and the Lasswell value dictionary. It contains 11,896 word senses, each tagged with markers that classify the word according to an extensible number of categories. There are markers for words of pleasure, pain, virtue, and vice; markers for words indicating overstatement and understatement; markers for places and locations; etc. The definitions of each word are very brief. Some example entries in GI are presented in Table 1. The General Inquirer category of interest to our work is CORRECT#1 H4Lvd Positiv Pstv Virtue Ovrst POSAFF Modif 21% adj: Accurate, proper CORRECT#2 H4Lvd Positiv Pstv Strng Work IAV TRNGAIN SUPV 54% verb: To make right, improve; to point out error (0) CORRECT#3 H4Lvd Positiv Pstv Virtue Ovrst POSAFF Modif 25% adv: ”Correctly” – properly, accurately CORRECT#4 H4Lvd Virtue TRNGAIN Modif 0% adj: ”Corrected” – made right Table 1: General Inquirer entries for the word correct. Positiv/Negativ. (The abbreviations Pstv/Ngtv in Table 1 are earlier versions of Positiv/Negativ.) A positive word corresponds to a favorable attitude; a negative one corresponds to a pejorative attitude. There are 1,915 words marked as Positiv (not including words for yes, which is a separate category of 20 entries), and 2,291 words marked as Negativ (not including the separate category no in the sense of refusal). An attitudinal distinction was asserted in our LKB of NS for each near-synonym in CTRW that was marked Positiv or Negativ in GI. In this paper, we focus on the attitudinal distinctions stored into our LKB of NS, acquired from CTRW and GI. For our near-synonyms, we extracted 1,519 attitudinal distinctions from GI, and 384 from CTRW. The information acquired from the two sources was merged and conflicts were resolved through a voting scheme. After merging, we were left with 1,709 attitudinal distinctions in our LKB of NS. The rest of the near-synonyms are considered neutral by default.