Entity-Centric Contextual Affective Analysis
Entity-Centric Contextual Affective Analysis
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DOI：
10.18653/v1/p19-1243
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Anjalie Field;Yulia Tsvetkov
中科院分区：
文献类型：
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作者：
Anjalie Field;Yulia Tsvetkov
虽然语境化的单词表征在许多NLP任务中改善了最先进的基准，但它们对面向社会的任务的潜在有用性在很大程度上仍未被探索。我们展示了如何语境化的词嵌入可以用来捕捉影响的人的肖像尺寸。我们评估我们的方法，定量，支持的影响词汇，定性，通过案例。我们发现，语境化的词表征确实编码了有意义的情感信息，但它们严重偏向于训练数据，这限制了它们在域内分析中的有用性。我们最终使用我们的方法来检查男性和女性的描绘差异。
While contextualized word representations have improved state-of-the-art benchmarks in many NLP tasks, their potential usefulness for social-oriented tasks remains largely unexplored. We show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people. We evaluate our methodology quantitatively, on held-out affect lexicons, and qualitatively, through case examples. We find that contextualized word representations do encode meaningful affect information, but they are heavily biased towards their training data, which limits their usefulness to in-domain analyses. We ultimately use our method to examine differences in portrayals of men and women.