Background & Overview Proposed Model Experimental Results Future Work Neural Network-Based Model for Japanese Predicate Argument Structure Analysis Tomohide Shibata, Daisuke Kawahara and Sadao Kurohashi (Kyoto University, Japan) Background & Overview Predicate-Argument Structure (PAS) analysis is a task of identifying “who does what to whom” in a sentence Japanese PAS analysis is considered as one of the most difficult basic tasks due to the following two phenomena: 1. Case disappearance: when a topic marker “は” is used, case markings disappear 2. Argument omission: arguments are often omitted SOTA: joint identification of all the arguments [Ouchi+15] - Scores for edges are calculated using the dot product of a sparse high-dimensional feature vector with a model parameter → A hand-crafted feature template is needed Our proposed model adopts Ouchi’s model as a base model, and is achieved by an NN-based two-stage method 1. Learn selectional preferences in an unsupervised manner using a large raw corpus 2. For an input sentence, we score a likelihood that a predicate takes an element as an argument using an NN framework Base Model [Ouchi+15] local score global score Proposed Model 1. Argument Prediction Model 2. NN-based Score Calculation No external knowledge is used in the base model Selectional preferences are the most important clue PASs are first extracted from an automatically-parsed raw Web corpus Learn selectional preferences using the extracted PASs based on an NN e.g., Calculate local and global scores using an NN framework Predicate/argument embeddings can capture the similar behavior of near-synonyms All the combinations of features in the input layer can be considered Experimental Results Evaluation set: Kyoto University Web Document Leads Corpus (5,000 Web documents) Gold morphologies, dependencies and named entities were used To consider “author” and “reader” as a referent, the two special nodes, [author] and [reader], were added in the graph of the base model 10M Web sentences were used for training the argument prediction model Future Work case analysis zero anaphora resolution Inter-sentential zero anaphora resolution Incorporate coreference resolution into our model