CLNov 19, 2015
Good, Better, Best: Choosing Word Embedding Context
arXiv:1511.06312v1
Originality Incremental advance
AI Analysis
This work addresses the challenge of enhancing word embeddings for natural language processing applications, though it appears incremental as it builds on existing methods.
The authors tackled the problem of learning vector representations for words and phrases by combining sentence context with dependency tree features, resulting in improved performance for supervised term-matching tasks.
We propose two methods of learning vector representations of words and phrases that each combine sentence context with structural features extracted from dependency trees. Using several variations of neural network classifier, we show that these combined methods lead to improved performance when used as input features for supervised term-matching.