Name
Abstract | ||
|---|---|---|
Target extraction is an important task in target-based sentiment analysis, which aims at identifying the boundary of target in given text. Previous works mainly utilize conditional random field (CRF) with a lot of handcraft features to recognize the target. However, it is hard to manually extract effective features to boost the performance of CRF-based methods. In this paper, we employ gated recurrent units (GRU) with label inference, to find valid label path for word sequence. At the same time, we find that character-level features play important roles in target extraction, and represent each word by concatenating word embedding and character-level representations which are learned via character-level GRU. Further, we capture boundary features of each word from its context words by convolution neural networks to assist the identification of the target boundary, since the boundary of a target is highly related to its context words. Experiments on two datasets show that our model outperforms CRF-based approaches and demonstrate the effectiveness of features learned from character-level and context words. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-99495-6_30 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Conditional random field,Pattern recognition,Sentiment analysis,Inference,Convolution,Computer science,Artificial intelligence,Concatenation,Word embedding,Artificial neural network | Conference | 11108 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dehong Ma | 1 | 61 | 4.73 |
| Sujian Li | 2 | 683 | 59.24 |
| Hou-Feng Wang | 3 | 611 | 53.83 |