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
dependency parsing
ジョン は パン を 買って
食べた。
John-TOP bread-ACC bought-and ate
φ-NOM
NOM
case analysis
 SOTA: joint identification of all the arguments [Ouchi+15]
- Scores for edges are calculated using the dot product of a sparse highdimensional 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
zero anaphora resolu on
が (ga) → nomina ve (NOM)
を (wo) → accusa ve (ACC)
に (ni) → da ve (DAT)
Base Model
[Ouchi+15]
ジョン
John
パン
bread
local score
a1
NOM global score
a2
買う
buy
a3 ACC
食べる
eat
a4 DAT
p1
買う
buy
p2
食べる
eat
ACC
NOM
DAT
a5
NULL
φ-ACC
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., p(ACC = bread|predicate = eat )
 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
local score
global score
scor el (x, e)
2
Wl
1
Wg
1
Wl
pred embed arg embed case
vp
va
other
arg. pred
features
score
scor eg (x, ei , ej )
2
Wg
pred embed
arg embed
v pj
v pi
v aj
v ai
casei
other
casej
features
vf g
vf l
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,
89.3
86.0
[author] and [reader], were
added
in
76.5
the graph of the base model
 10M Web sentences were used for
training the argument prediction
model
ACC
86.0
これまで 生産してきた 商品 の 中から、
89.3
so far
76.5
produce
goods-GEN
among
画像 で いくつか 紹介していきます。
image-INS
49.7
53.4
42.1
some
introduce
[author] NOM
goods ACC
[reader] DAT
case analysis zero anaphora resolution
49.7
42.1
53.4
Future Work
 Inter-sentential zero anaphora
resolution
 Incorporate coreference resolution
into our model