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Comprehensive analysis of aspect term extraction methods using various text embeddings

机译：使用各种文本嵌入的综合术语提取方法综合分析

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摘要

Recently, a variety of model designs and methods have blossomed in the context of the sentiment analysis domain. However, there is still a lack of comprehensive studies of Aspect-based Sentiment Analysis. We want to fill this gap and propose a comparison with ablation analysis of Aspect Term Extraction using various text embeddings methods. We particularly focused on simple architectures based on long short-term memory (LSTM) with optional conditional random field (CRF) enhancement using different pre-trained word embeddings. Moreover, we analyzed the influence on the performance of extending the word vectorization step with character-based word embeddings. The experimental results on SemEval datasets revealed that bi-directional long short-term memory (BiLSTM) could be used as a very good predictor, even comparing to very sophisticated and complex models using huge word embeddings or language models. We presented a comprehensive analysis of various customizations of LSTM-based architecture and word/character embeddings that could be used as a guideline to choose the best model version for particular user needs.
机译：最近，各种模型设计和方法在情感分析域的背景下蓬勃发展。然而，仍然缺乏对基于宽度的情绪分析的综合研究。我们希望使用各种文本嵌入方法填补这种差距并提出与ASPETS术语提取的消融分析的比较。我们特别专注于使用不同预先训练的单词嵌入的可选条件随机字段（CRF）增强的长短短期内存（LSTM）的简单架构。此外，我们分析了对扩展与基于字符的单词嵌入来扩展字矢量化步骤的性能的影响。 Semeval Datasets的实验结果显示，双向长期短期记忆（BILSTM）可以用作非常好的预测因子，甚至与使用巨大的单词嵌入或语言模型的非常复杂和复杂的模型相比。我们对LSTM的架构和单词/字符嵌入的各种自定义进行了全面分析，可以用作选择特定用户需求的最佳型号版本的指导。

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