Incorporating Both Distributional and Relational Semantics in Word Representations
Table 3: Comparison of parsers using different embedding features. Labeled Arc Score (LAS) on five web domains and their average are reported.
['Embedding', 'AVERAGE', 'Answers', 'Emails', 'Newsgroups', 'Reviews', 'Weblogs']
We observe: Incorporating embeddings from joint objective training always helps; all of these embeddings improve upon the case of no embeddings (None) in all five domains. This is a nice result considering that our embeddings are trained on Google Books, not SANCL, and have a 9-13% token out-of-vocabulary rate on the data. In contrast, improvements from embeddings trained from a single relational objective are mixed, and are in general poorer than those trained from a single distributional objective (NLM). This suggests distributional information may be more effective for this task. The best results are achieved by the joint models NLM+GD (average LAS of 76.18) and NLM+NTN (76.14). The improvement over NLM (76.03) is not large, but we believe this is a promising result nevertheless; it implies that our joint objective does indeed complement the strong results of distributional semantics.