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| Content Provider | ACM Digital Library |
|---|---|
| Author | Gupta, Manish Varma, Vasudeva J, Ganesh |
| Abstract | Doc2Sent2Vec is an unsupervised approach to learn low-dimensional feature vector (or embedding) for a document. This embedding captures the semantics of the document and can be fed as input to machine learning algorithms to solve a myriad number of applications in the field of data mining and information retrieval. Some of these applications include document classification, retrieval, and ranking. The proposed approach is two-phased. In the first phase, the model learns a vector for each sentence in the document using a standard word-level language model. In the next phase, it learns the document representation from the sentence sequence using a novel sentence-level language model. Intuitively, the first phase captures the word-level coherence to learn sentence embeddings, while the second phase captures the sentence-level coherence to learn document embeddings. Compared to the state-of-the-art models that learn document vectors directly from the word sequences, we hypothesize that the proposed decoupled strategy of learning sentence embeddings followed by document embeddings helps the model learn accurate and rich document representations. We evaluate the learned document embeddings by considering two classification tasks: scientific article classification and Wikipedia page classification. Our model outperforms the current state-of-the-art models in the scientific article classification task by ?12.07% and the Wikipedia page classification task by ?6.93%, both in terms of F1 score. These results highlight the superior quality of document embeddings learned by the Doc2Sent2Vec approach. |
| Starting Page | 809 |
| Ending Page | 812 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450340694 |
| DOI | 10.1145/2911451.2914717 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-07-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Distributed representation Sentence embedding Document modeling Machine learning Document embedding Word embedding |
| Content Type | Text |
| Resource Type | Article |
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