Abstract
Choosing the best word or phrase for a given context from among the candidate near-synonyms, such as slim and skinny, is a difficult language generation problem. In this paper, we describe approaches to solving an instance of this problem, the lexical gap problem, with a particular focus on affect and subjectivity; to do this we draw upon techniques from the sentiment and subjectivity analysis fields. We present a supervised approach to this problem, initially with a unigram model that solidly outperforms the baseline, with a 6.8% increase in accuracy. The results to some extent confirm those from related problems, where feature presence outperforms feature frequency, and immediate context features generally outperform wider context features. However, this latter is somewhat surprisingly not always the case, and not necessarily where intuition might first suggest; and an analysis of where document-level models are in some cases better suggested that, in our corpus, broader features related to the 'tone' of the document could be useful, including document sentiment, document author, and a distance metric for weighting the wider lexical context of the gap itself. From these, our best model has a 10.1% increase in accuracy, corresponding to a 38% reduction in errors. Moreover, our models do not just improve accuracy on affective word choice, but on non-affective word choice also.
| Language | English |
|---|---|
| Pages | 97-134 |
| Number of pages | 38 |
| Journal | Natural Language Engineering |
| Volume | 22 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2016 |
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Predicting word choice in affective text. / Gardiner, M.; Dras, M.
In: Natural Language Engineering, Vol. 22, No. 1, 01.01.2016, p. 97-134.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Predicting word choice in affective text
AU - Gardiner, M.
AU - Dras, M.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Choosing the best word or phrase for a given context from among the candidate near-synonyms, such as slim and skinny, is a difficult language generation problem. In this paper, we describe approaches to solving an instance of this problem, the lexical gap problem, with a particular focus on affect and subjectivity; to do this we draw upon techniques from the sentiment and subjectivity analysis fields. We present a supervised approach to this problem, initially with a unigram model that solidly outperforms the baseline, with a 6.8% increase in accuracy. The results to some extent confirm those from related problems, where feature presence outperforms feature frequency, and immediate context features generally outperform wider context features. However, this latter is somewhat surprisingly not always the case, and not necessarily where intuition might first suggest; and an analysis of where document-level models are in some cases better suggested that, in our corpus, broader features related to the 'tone' of the document could be useful, including document sentiment, document author, and a distance metric for weighting the wider lexical context of the gap itself. From these, our best model has a 10.1% increase in accuracy, corresponding to a 38% reduction in errors. Moreover, our models do not just improve accuracy on affective word choice, but on non-affective word choice also.
AB - Choosing the best word or phrase for a given context from among the candidate near-synonyms, such as slim and skinny, is a difficult language generation problem. In this paper, we describe approaches to solving an instance of this problem, the lexical gap problem, with a particular focus on affect and subjectivity; to do this we draw upon techniques from the sentiment and subjectivity analysis fields. We present a supervised approach to this problem, initially with a unigram model that solidly outperforms the baseline, with a 6.8% increase in accuracy. The results to some extent confirm those from related problems, where feature presence outperforms feature frequency, and immediate context features generally outperform wider context features. However, this latter is somewhat surprisingly not always the case, and not necessarily where intuition might first suggest; and an analysis of where document-level models are in some cases better suggested that, in our corpus, broader features related to the 'tone' of the document could be useful, including document sentiment, document author, and a distance metric for weighting the wider lexical context of the gap itself. From these, our best model has a 10.1% increase in accuracy, corresponding to a 38% reduction in errors. Moreover, our models do not just improve accuracy on affective word choice, but on non-affective word choice also.
UR - http://www.scopus.com/inward/record.url?scp=84949625537&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DP0558852
U2 - 10.1017/S1351324915000157
DO - 10.1017/S1351324915000157
M3 - Article
VL - 22
SP - 97
EP - 134
JO - Natural Language Engineering
T2 - Natural Language Engineering
JF - Natural Language Engineering
SN - 1351-3249
IS - 1
ER -