Paulina Toro Isaza, Guangxuan Xu, et al.
ACL 2023
Previous work on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013b) use syntactic preposition patterns to calculate word relatedness. However, such patterns only consider NPs' head nouns and hence do not fully capture the semantics of NPs. Recently, Hou (2018) created word embeddings (embeddings PP) to capture associative similarity (i.e., relatedness) between nouns by exploring the syntactic structure of noun phrases. But embeddings PP only contains word representations for nouns. In this paper, we create new word vectors by combining embeddings PP with GloVe. This new word embeddings (embeddings bridging) are a more general lexical knowledge resource for bridging and allow us to represent the meaning of an NP beyond its head easily. We therefore develop a deterministic approach for bridging anaphora resolution, which represents the semantics of an NP based on its head noun and modifications. We show that this simple approach achieves the competitive results compared to the best system in Hou et al. (2013b) which explores Markov Logic Networks to model the problem. Additionally, we further improve the results for bridging anaphora resolution reported in Hou (2018) by combining our simple deterministic approach with Hou et al. (2013b)'s best system MLN II.
Paulina Toro Isaza, Guangxuan Xu, et al.
ACL 2023
Khalid Al-Khatib, Yufang Hou, et al.
AAAI 2020
Yufang Hou
NAACL 2018
Myles Foley, Ambrish Rawat, et al.
ACL 2023