Enhanced Word Representations for Bridging Anaphora Resolution
This addresses a specific challenge in natural language processing for tasks requiring associative knowledge, but it is incremental as it builds on existing embedding methods.
The paper tackled the problem of bridging anaphora resolution by creating word embeddings that capture associative similarity instead of semantic similarity, achieving around 30% accuracy on the ISNotes corpus and a substantial gain over the state-of-the-art system.
Most current models of word representations(e.g.,GloVe) have successfully captured fine-grained semantics. However, semantic similarity exhibited in these word embeddings is not suitable for resolving bridging anaphora, which requires the knowledge of associative similarity (i.e., relatedness) instead of semantic similarity information between synonyms or hypernyms. We create word embeddings (embeddings_PP) to capture such relatedness by exploring the syntactic structure of noun phrases. We demonstrate that using embeddings_PP alone achieves around 30% of accuracy for bridging anaphora resolution on the ISNotes corpus. Furthermore, we achieve a substantial gain over the state-of-the-art system (Hou et al., 2013) for bridging antecedent selection.