1. 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