Paper: Evaluating Neural Word Representations in Tensor-Based Compositional Settings

ACL ID D14-1079
Title Evaluating Neural Word Representations in Tensor-Based Compositional Settings
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

We provide a comparative study be- tween neural word representations and traditional vector spaces based on co- occurrence counts, in a number of com- positional tasks. We use three differ- ent semantic spaces and implement seven tensor-based compositional models, which we then test (together with simpler ad- ditive and multiplicative approaches) in tasks involving verb disambiguation and sentence similarity. To check their scala- bility, we additionally evaluate the spaces using simple compositional methods on larger-scale tasks with less constrained language: paraphrase detection and di- alogue act tagging. In the more con- strained tasks, co-occurrence vectors are competitive, although choice of composi- tional method is important; on the larger- scale tasks, they are outperformed by ne...