Dependency Based Embeddings for Sentence Classification Tasks
Dependency Based Embeddings for Sentence Classification Tasks
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DOI：
10.18653/v1/n16-1175
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发表时间：
2016-06
期刊：
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通讯作者：
Alexandros Komninos;S. Manandhar
中科院分区：
文献类型：
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作者：
Alexandros Komninos;S. Manandhar
我们比较了基于标准窗口的skipgram模型、使用依赖关系上下文特征训练的skipgram模型和利用依赖关系图中附加信息的新颖skipgram变体的不同词嵌入。我们探讨了不同类型的词嵌入在词相似和句子分类任务中的有效性。我们考虑了三种常见的句子分类任务：TREC数据集上的问题类型分类、斯坦福情感树库上的二元情感分类和SemEval 2010数据集上的语义关系分类。对于每个任务，我们使用三种不同的分类方法：支持向量机，卷积神经网络和长短期记忆网络。我们的实验表明，在大多数情况下，基于依赖的嵌入优于标准的基于窗口的嵌入，而使用依赖上下文嵌入作为附加特征，无论使用哪种分类方法，都可以提高所有任务的性能。我们的文件和代码可以在https上找到：
We compare different word embeddings from a standard window based skipgram model, a skipgram model trained using dependency context features and a novel skipgram variant that utilizes additional information from dependency graphs. We explore the effectiveness of the different types of word embeddings for word similarity and sentence classification tasks. We consider three common sentence classification tasks: question type classification on the TREC dataset, binary sentiment classification on Stanford’s Sentiment Treebank and semantic relation classification on the SemEval 2010 dataset. For each task we use three different classification methods: a Support Vector Machine, a Convolutional Neural Network and a Long Short Term Memory Network. Our experiments show that dependency based embeddings outperform standard window based embeddings in most of the settings, while using dependency context embeddings as additional features improves performance in all tasks regardless of the classification method. Ourembeddings and code are available at https: