Opinion Mining with Deep Contextualized Embeddings
Opinion Mining with Deep Contextualized Embeddings
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DOI：
10.18653/v1/n19-3006
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Wen-Bin Han;N. Kando
中科院分区：
文献类型：
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作者：
Wen-Bin Han;N. Kando
检测意见表达是意见挖掘中的一项潜在而重要的任务，可以扩展到高级任务。本文将意见表达检测视为一项序列标注任务，并将不同的深度上下文化嵌入器应用到该框架中，包括双向长期短期记忆(BiLSTM)和条件随机场(CRF)。我们的实验结果表明，使用不同的单词嵌入可以产生对比结果，并且该模型在深度上下文嵌入的情况下可以获得显著的分数。特别是，使用BERT嵌入器可以显著超过使用Elmo嵌入器。
Detecting opinion expression is a potential and essential task in opinion mining that can be extended to advanced tasks. In this paper, we considered opinion expression detection as a sequence labeling task and exploited different deep contextualized embedders into the state-of-the-art architecture, composed of bidirectional long short-term memory (BiLSTM) and conditional random field (CRF). Our experimental results show that using different word embeddings can cause contrasting results, and the model can achieve remarkable scores with deep contextualized embeddings. Especially, using BERT embedder can significantly exceed using ELMo embedder.