Topic
Word embedding
About: Word embedding is a research topic. Over the lifetime, 4683 publications have been published within this topic receiving 153378 citations. The topic is also known as: word embeddings.
Papers
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24 Oct 2016
TL;DR: This paper proposes a linked document embedding framework LDE, which combines link and label information with content information to learn document representations for classification, and experiments demonstrate the effectiveness of the proposed framework.
Abstract: Word and document embedding algorithms such as Skip-gram and Paragraph Vector have been proven to help various text analysis tasks such as document classification, document clustering and information retrieval. The vast majority of these algorithms are designed to work with independent and identically distributed documents. However, in many real-world applications, documents are inherently linked. For example, web documents such as blogs and online news often have hyperlinks to other web documents, and scientific articles usually cite other articles. Linked documents present new challenges to traditional document embedding algorithms. In addition, most existing document embedding algorithms are unsupervised and their learned representations may not be optimal for classification when labeling information is available. In this paper, we study the problem of linked document embedding for classification and propose a linked document embedding framework LDE, which combines link and label information with content information to learn document representations for classification. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of link and label information in the proposed framework LDE.
94 citations
Posted Content•
TL;DR: This study proposes to use BLSTM-RNN with word embedding for part-of-speech (POS) tagging task and can also achieve a good performance comparable with the Stanford POS tagger.
Abstract: Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for tagging sequential data, e.g. speech utterances or handwritten documents. While word embedding has been demoed as a powerful representation for characterizing the statistical properties of natural language. In this study, we propose to use BLSTM-RNN with word embedding for part-of-speech (POS) tagging task. When tested on Penn Treebank WSJ test set, a state-of-the-art performance of 97.40 tagging accuracy is achieved. Without using morphological features, this approach can also achieve a good performance comparable with the Stanford POS tagger.
93 citations
TL;DR: A joint architecture which places of RNN at first for capturing long-term dependencies with CNNs using global average pooling layer while on top a word embedding method using GloVe procured by unsupervised learning in the light of substantial twitter corpora to deal with this problem.
92 citations
17 Jul 2018
TL;DR: An unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task and extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so they can be better translated by machines.
Abstract: Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation. Current word embedding based metaphor identification models cannot identify the exact metaphorical words within a sentence. In this paper, we propose an unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task. Our model extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so that they can be better translated by machines. We evaluated this with two popular translation systems for English to Chinese, showing that our model improved the systems significantly.
92 citations
TL;DR: A novel kernel k-means clustering method incorporated with a word embedding model to create a solution that effectively extracts topics from bibliometric data is proposed.
92 citations