Intra-sentential Zero Anaphora Resolution using Subject Sharing Recognition

Abstract

In this work, we improve the performance of intra-sentential zero anaphora resolution in Japanese using a novel method of recognizing subject sharing relations. In Japanese, a large portion of intrasentential zero anaphora can be regarded as subject sharing relations between predicates, that is, the subject of some predicate is also the unrealized subject of other predicates. We develop an accurate recognizer of subject sharing relations for pairs of predicates in a single sentence, and then construct a subject shared predicate network, which is a set of predicates that are linked by the subject sharing relations recognized by our recognizer. We finally combine our zero anaphora resolution method exploiting the subject shared predicate network and a state-ofthe-art ILP-based zero anaphora resolution method. Our combined method achieved a significant improvement over the the ILPbased method alone on intra-sentential zero anaphora resolution in Japanese. To the best of our knowledge, this is the first work to explicitly use an independent subject sharing recognizer in zero anaphora resolution.

Intra-Sentential Subject Zero Anaphora Resolution using Multi-Column Convolutional Neural Network

“…If a method provides an incorrect zero anaphoric relation, a correct relation for the same zero anaphor will never be provided in its output. Also, note that the average precision of each method was calculated by averaging the precisions at the available recall 10 The PR-curve of Iida et al (2015)'s method was not plotted because it does not provide the score of each zero anaphoric relation. 11 The global score provided by Ouchi's method becomes greater based on the number of predicate-argument pairs in a sentence.…”

“…Recent work by Iida et al (2015) newly introduced a sub-problem of zero anaphora resolution, subject sharing recognition, which is the task that judges whether two predicates have the same subject. In their method, a network of subject sharing predicates is created by their subject sharing recognizer, and then zero anaphora resolution is performed by propagating a subject to the unrealized subject positions through the path in the network.…”

“…The remaining two baselines are Ouchi et al (2015)'s global optimization method and Iida et al (2015)'s method based on subject sharing recognition. Note that Ouchi's method outputs predicateargument relations for three grammatical roles (subj, obj, iobj), but for this evaluation we used only the outputs related to intra-sentential subject zero anaphora resolution.…”

Chinese Zero Pronoun Resolution with Deep Memory Network

“…Ferrández and Peral (2000) propose a set of hand-crafted rules for resolving ZPs in Spanish texts. Recently, supervised approaches have been widely exploited for ZP resolution in Korean (Han, 2006), Italian (Iida and Poesio, 2011) and Japanese (Isozaki and Hirao, 2003;Iida et al, 2006Iida et al, , 2007Imamura et al, 2009;Sasano and Kurohashi, 2011;Iida and Poesio, 2011;Iida et al, 2015). Iida et al (2016) propose a multi-column convolutional neural network for Japanese intra-sentential subject zero anaphora resolution, where both the surface word sequence and dependency tree of a target sentence are exploited as clues in their model.…”

A preliminary study in zero anaphora coreference resolution for Polish

“…Chen and Ng (2015) have proposed an unsupervised probabilistic model for the joint identification and resolution of zero pronouns by exploiting information about discourse salience, whereas Yin, Zhang, Zhang, and Liu (2016) have proposed a deep neural network model that eliminates the need for hand-crafted features, by generating an abstract representation. Finally, Iida, Torisawa, Hashimoto, Oh, and Kloetzer (2015) have presented an approach to improving zero anaphora resolution in Japanese by using a novel method of subject sharing recognition.…”