# Enhanced Word Representations for Bridging Anaphora Resolution

arXiv: https://arxiv.org/abs/1803.04790 · HTML: https://mlsanity.com/paper/1803.04790/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Holmes: A Benchmark to Assess the Linguistic Competence of Language Models](https://mlsanity.com/paper/2404.18923/) (2024)
- [Probing for Bridging Inference in Transformer Language Models](https://mlsanity.com/paper/2104.09400/) (2021)
- [Document Embedding for Scientific Articles: Efficacy of Word Embeddings vs TFIDF](https://mlsanity.com/paper/2107.05151/) (2021)

## Abstract (arXiv)

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.