    \documentclass[english]{jnlp_2.1}

\usepackage{jnlpbbl_1.3}
\usepackage[dvipdfmx]{graphicx}
\usepackage{amsmath}
\usepackage[multi]{otf}
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\usepackage{url}


\Volume{25}
\Number{2}
\Month{March}
\Year{2018}

\received{2017}{6}{21}
\revised{2017}{9}{10}
\accepted{2017}{11}{5}

\setcounter{page}{201}

\esubject{Paper}
\etitle{Improving Chinese Semantic Role Labeling using High-quality Surface and Deep Case Frames}
\eauthor{Gongye Jin\affiref{Author_1} \and Daisuke Kawahara\affiref{Author_1} \and Sadao Kurohashi\affiref{Author_1}} 

\eabstract{
This paper presents a method for improving semantic role labeling (SRL) using a large amount of automatically acquired knowledge. We acquire two varieties of knowledge, which we call surface case frames and deep case frames. Although the surface case frames are compiled from syntactic parses and can be used as rich syntactic knowledge, they have limited capability for resolving semantic ambiguity. To compensate for the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles. We also consider quality management for both types of knowledge in order to get rid of the noise brought from the automatic analyses. The experimental results show that Chinese SRL can be improved using automatically acquired knowledge and the quality management shows a positive effect on this task.
}
\ekeywords{Semantic Role Labeling, Surface Case Frames, Deep Case Frames}

\headauthor{Jin, Kawahara, Kurohashi}
\headtitle{Semantic Role Labeling Using Case Frames}


\affilabel{Author_1}{}{Graduate School of Informatics, Kyoto University}


\begin{document}

\maketitle

\section{INTRODUCTION}
\label{intro}

Semantic role labeling (SRL) is regarded as a task that is intermediate between syntactic analysis and semantic analysis in natural language processing (NLP).
The main goal of SRL is to extract a proposition from a sentence about {\it who} does {\it what } to {\it whom}, {\it when, where} and {\it why}.
By using semantic roles, the complex expression of a sentence is then interpreted as an {\it event} and its {\it participants}
(i.e., a predicate and arguments such as {\it agent, patient, locative, temporal} and {\it manner}). Unlike syntactic level surface cases (i.e., dependency labels such as subject and object), semantic roles can be regarded as a deep case representation of predicates. Because of its ability to abstract the meaning of a sentence,
SRL has been applied to many NLP applications, including information extraction \cite{christensen-EtAl:2010:FAMLBR}, question answering \cite{pizzato-molla:2008:IR4QA} and machine translation \cite{liu-gildea:2010:PAPERS}. 

Semantically annotated corpora, such as FrameNet \cite{fillmore01:dsemlex} and PropBank \cite{Kingsbury02fromtreebank}, make this type
of automatic semantic structure analysis feasible by using supervised machine learning methods. 
There exist such corpora not only in English but also in Chinese. We focus on Chinese SRL in this study.
However, Chinese SRL has two major issues. Firstly it is expensive to enlarge manually annotated corpora to learn a more accurate model, which is a common issue in almost all supervised approaches. Secondly, due to the linguistic nature of Chinese, Chinese SRL achieves lower performance than English SRL due to flexible word order, omission of syntactic components and so forth. Moreover, experiments show that automatic SRL systems strongly depend on syntactic information. 
In practice, Chinese SRL systems suffer from errors propagated from the lower-level syntactic analyses, such as word segmentation, POS tagging, and dependency parsing. 
Although some studies use automatic analyses of unlabeled corpora to enrich the training data to solve the first problem \cite{furstenau-lapata:2009:EACL}, 
accumulated errors in such automatic analyses inevitably cause negative effects. 

\begin{figure}[b]
\begin{center}
\includegraphics{25-2ia2f1.eps}
\end{center}
\caption{Overview of the framework}
\label{flow}
\end{figure}

In this paper, we focus on Chinese SRL and solve the problems mentioned above by using high-quality knowledge automatically acquired from a large-scale raw corpus.
We utilize two types of additional knowledge. The first type is compiled using automatic syntactic analysis (dependency parsing in our case) and is named {\bf surface case frames} which are not expressive at the semantic level. In order to compensate for the drawback of surface case frames, we also compile another type of knowledge using automatic semantic roles. 
We call this type of knowledge {\bf deep case frames}. We illustrate the whole framework in Figure~\ref{flow}. We first apply dependency parsing to a large-scale raw corpus.
Considering the inevitable noises from automatic analyses, we utilize an automatic selection method to select dependencies of high quality which is indicated as step~\textcircled{\raisebox{-0.8pt}{1}}. As shown in step \textcircled{\raisebox{-0.8pt}{2}}, using high-quality dependencies we compile surface case frames and in step \textcircled{\raisebox{-0.8pt}{3}} we use them to improve an SRL system. After applying SRL on a raw corpus, we then use a similar seleciton approach for automatic semantic roles as shown in step \textcircled{\raisebox{-0.8pt}{4}}. Selected high-quality semantic roles are then used as deep case frames to improve the SRL system in step \textcircled{\raisebox{-0.8pt}{5}}. In order to show that automatically extracted knowledge is beneficial and the quality management is indispensable, we compile both types of knowledge in different quality in our experiments and apply them to Chinese SRL. To represent Chinese surface cases, we utilize grammatical functions, such as ``subject'', ``direct object'', and ``adverbial''. We use PropBank arguments, such as ``A0'' and ``A1'', to represent Chinese deep cases.

This paper is organized as follows. Section \ref{srl:related} reviews some studies relevant to our approach.
Section \ref{srl:baseline} describes the baseline SRL models.
Section \ref{srl:surface} describes the approach of using surface case frames to improve SRL.
Section \ref{srl:problem} discusses the main problem of surface case frames.
Section \ref{srl:deep} presents an approach of using deep case frames to improve SRL.
Section \ref{srl:exp} gives a detailed description of experiments, along with the results and discussion.
Finally, Section \ref{srl:conclusion} presents a conclusion of our approach and future work.


\section{RELATED WORK}
\label{srl:related}

The CoNLL-2009 shared task \cite{hajivc-EtAl:2009:CoNLL-2009-ST} features a substantial number of studies on SRL that used Propbank as one of the resources.
The tasks can be categorized into two types: joint learning of syntactic parsing along with SRL \cite{tang-EtAl:2009:CoNLL-2009-ST,morante-vanasch-vandenbosch:2009:CoNLL-2009-ST}; and SRL-only task \cite{zhao-EtAl:2009:CoNLL-2009-ST1,bjorkelund-hafdell-nugues:2009:CoNLL-2009-ST}.
The first type learns a unique model for syntactic parsing and SRL jointly. This type of framework has the ability to use SRL information in syntactic parsing for improvement,
but needs a much larger search space for decoding.
The SRL-only task uses automatic morphological and syntactic information as the input in order to judge which token plays what kind of semantic role. 
We focus on the SRL-only task. Our framework is based on those used by \citeA{bjorkelund-hafdell-nugues:2009:CoNLL-2009-ST} and Yang and Zong (2014)\nocite{yang-zong:2014:EMNLP2014}.

There are several studies that use additional knowledge to improve syntactic and semantic tasks. 
McClosky, Charniak and Johnson (2006)\nocite{mcclosky-charniak-johnson:2006:COLACL} used an additional unlabeled corpus to reduce data sparsity. 
At the syntactic level of NLP, rich knowledge such as predicate-argument structures and case frames is a strong backup
for various kinds of tasks. Case frames, which clarify relations between a predicate and its arguments, can
support tasks ranging from fundamental analysis, such as dependency parsing and
word similarity calculation, to multilingual applications, such as machine translation.
Japanese case frames have been successfully compiled \cite{Kawahara:2006}, where each case slot is represented
as its case marker in Japanese such as `ga', `wo', and `ni'.
For the case frames of other languages such as English and Chinese,
because there are no such case markers that can help clarify syntactic structures,
instead of using case markers like in Japanese,
syntactic surface cases (i.e., subject, object, prepositional phrase, etc.)
are used for argument representation \cite{JIN14.828}.
Case frames can be automatically acquired using a different method such as distributional similarity \cite{JIN14.828} and Chinese Restaurant Process (CRP) \cite{kawahara-EtAl:2014:EACL} for different languages.
In our work, we employ such syntactic level knowledge which uses surface cases for argument representation to help SRL.

One basic idea of semi-supervised SRL is to automatically annotate unlabeled data using a simple classifier trained on original training data \cite{furstenau-lapata:2009:EACL}.
Since there is a substantial amount of error propagation in the SRL pipeline, the additional automatic semantic roles are not guaranteed to be of good quality.
Some studies assume that sentences that are syntactically and lexically similar are likely to share the same frame-semantic structure  \cite{furstenau-lapata:2009:EACL}.
This allows them to project semantic role information to unlabeled sentences using alignments.
However, the computation of these alignments requires additional information such as word similarity, whose quality is language dependent.
Less sparse features capturing lexical information of words can be also used for semi-supervised learning of SRL.
Such lexical representation can be learned from unlabeled data \cite{Bengio:2003}. 
Deschacht and Moens (2009)\nocite{deschacht-moens:2009:EMNLP} used word similarity learned from unlabeled data as additional features for SRL.
Word embeddings have also been used in several NLP tasks including syntactic parsing \cite{socher-EtAl:2013:ACL2013}, machine translation \cite{zou-EtAl:2013:EMNLP} and SRL \cite{Ronan:2011}.
Instead of using word-level lexical knowledge, our work uses syntactic and semantic knowledge, i.e., case frames.
Word embeddings can also be incorporated into our method but we leave this for future work.
Zapirain, Agirre, and M\`{a}rquez (2009)\nocite{zapirain-agirre-marquez} used selectional preferences to improve SRL. Their study is similar to our approaches but the quality of selectional preferences was not considered at all.


\section{BASELINE SRL MODELS}
\label{srl:baseline}

Our baseline SRL models are based on previous studies \cite{zhao-EtAl:2009:CoNLL-2009-ST1,morante-vanasch-vandenbosch:2009:CoNLL-2009-ST}. In these studies, an SRL pipeline\footnote[1]{Predicate identification (PI) was not concerned in our experiments because we used the data from CoNLL-2009 shared task, in which the target predicates are given.} can be divided into three main parts: predicate disambiguation (PD), argument identification (AI), and argument classification (AC). 
In the PD step, the main goal is to identify the ``sense id'' of each given predicate.
The AI step mainly focuses on judging whether each argument is semantically related to each predicate in a sentence.
Based on the results of the AI step, the AC step assigns a semantic role to each semantically related argument.
Basically, the PD step and the AC step are regarded as multi-class classification problems, while the AI step is a binary classification problem.

In the PD step, because the sense id of a certain predicate is meaningless for other predicates,
classifiers for PD are trained separately for each predicate.
We basically use the feature set proposed by \citeA{bjorkelund-hafdell-nugues:2009:CoNLL-2009-ST}.
During the prediction, there are some predicates which have not been seen in the training data. We label the sense of those unseen predicates using the default sense,
which is `01' in our work. We list the basic features for each step in Table~\ref{PDF} and \ref{AF}.

\begin{table}[b]
\caption{Features for PD}
\label{PDF}
\input{02table01.tex}
\end{table}
\begin{table}[t]
\caption{Features for AI and AC (\dag\hspace{0.1cm}marks stand for the features we proposed)}
\label{AF}
\input{02table02.tex}
\end{table}


\section{APPLYING HIGH-QUALITY SURFACE CASE FRAMES TO SRL}
\label{srl:surface}

\subsection{High-quality Dependency Selection}

Dependency parsing has been widely employed for knowledge acquisition related to predicate-argument structures.
The dependency parsing performance determines the quality of acquired knowledge, regardless of target languages.
Reducing dependency parsing errors and selecting high-quality dependencies are of primary importance. This method corresponds to step \textcircled{\raisebox{-0.8pt}{1}} in Figure~\ref{flow}. We employ the dependency selection method decribed in \cite{Jin:2013:ijcnlp}. They used a single set of dependency labeled corpus (a treebank), a part of which was used to train a base dependency parser.
Another part of the labeled corpus was used to apply automatic dependency parsing. 
By comparing the gold standard data and the automatic parses, 
correct dependencies were collected as positive examples and incorrect dependencies were collected as negative examples.
Then selecting high-quality dependencies was regarded as a binary classification problem.
To conduct such binary classification, we employed a set of basic features from Yu, Kawahara, and Kurohashi (2008)\nocite{Yu:2008}. 
In addition to these basic features, Jin, Kawahara, and Kurohashi (2013)\nocite{Jin:2013:ijcnlp} considered context features
that are thought to affect parsing performance. Since the input for the high-quality dependency selection method is a dependency tree, tree
features are used to identify dependency quality. Also, some dependency parsers output the score of each dependency (i.e., edge confidence value) during the parsing process.
They used the real value of the score as an additional feature. We first apply this approach to select high-quality dependencies from automatic parses.

\begin{table}[b]
\caption{Examples of Chinese surface case frames}
\label{xie}
\input{02table03.tex}
\end{table}


\subsection{High-quality Surface Case Frame Construction}

High-quality surface case frame construction step is marked as number \textcircled{\raisebox{-0.8pt}{2}} in Figure~\ref{flow}.
After applying dependency parsing on a large-scale raw corpus, predicate-argument structures (PASs) are extracted using the high-quality dependencies.
Arguments are represented by their dependency labels (i.e., subject, object, etc.)
For each predicate, all the PASs are clustered into different case frames to reflect different semantic usages.
We show an example of case frames for the verb `\UTFC{8C22}' in Table~\ref{xie}, which has multiple meanings.
In this example, 'nsubj' stands for nominal subject, 'dobj' means direct object and 'ad' is used to represent adverbial modifier.
The detailed explanation of the dependency labels can be found in Stanford typed dependencies manual.\footnote[2]{https://nlp.stanford.edu/software/dependencies\_manual.pdf (Section 3)}
`\UTFC{8C22}(1)' is the case frame used to represent the sense of `withering of flower'.
Similarly, the sense of `\UTFC{8C22}' which means `to thank' is represented by case frame `\UTFC{8C22}(2)'.
`\UTFC{8C22}(3)' is the case frame for the sense of `curtain call'. In other words, case frames are knowledge that solves word sense disambiguation (WSD) by clustering the PASs.
We applied the CRP method described by \cite{kawahara-EtAl:2014:EACL} for clustering the high-quality PASs to compile high-quality case frames.


\subsection{Surface Case Features for SRL}

As shown in step \textcircled{\raisebox{-0.8pt}{3}} in Figure~\ref{flow}, we utilize surface caseframes to assist the baseline SRL system.
From the surface case PASs, we extract four types of additional features, for both AI and AC steps in SRL.
These features are described in the upper part of Table~\ref{table:surfaceFeature}. We use binned values (i.e., high, middle and low) for all of the feature values calculated from the knowledge. 
More specifically, for each type of feature, we define the first, second and third tertile of all the feature values as low, middle and high correspondingly.
Surface case frames are clustered PASs according to each predicate's semantic usage.
Therefore, instead of utilizing all the predicate-argument structures,
it is intuitive to use the predicate-argument structures only from the corresponding case frames.
So we also create four types of features extracted from case frames. These features are listed in the lower part of Table~\ref{table:surfaceFeature}.

\begin{table}[b]
\caption{Surface case features for SRL}
\label{table:surfaceFeature}
\input{02table04.tex}
\end{table}

\begin{figure}[t]
\begin{center}
\includegraphics{25-2ia2f2.eps}
\end{center}
\caption{Overview of mapping case frames to PropBank sense}
\label{mapping}
\vspace{-0.5\Cvs}
\end{figure}

Note that a case frame ID and a PropBank sense ID do not correspond to each other.
In practice, the number of case frames is always larger than the number of senses in PropBank for each verb. 
As a result, a mapping process that aligns case frame id(s) to PropBank verb sense is applied.
First, we assign dependency labels to the PropBank corpus using the Stanford parser.
We then calculate the similarity between a PropBank sense and a case frame by measuring the PAS similarity.
As shown in the left part of Figure~\ref{mapping}, for a certain predicate with a PropBank sense ID.
we represent the predicate in each sense by using the collection of all the instances in each grammatical function slot.
Each predicate with a PropBank sense ID is then transformed into a vector space, which we name PAS vector. 
The same transformation is applied to case frames. 
A PAS vector is the concatenation of each grammatical function vector. 
To form a grammatical function vector ${\bf v}_{gf}$, we simply take the average of weighted summation of the word vectors within the case slot using Equation (1), where ${\bf v}_i$ denotes a word vector for each instance and $f_i$ denotes the corresponding frequency.
\begin{equation}
{\bf v}_{gf} = \frac{\sum_{i=1}^{n} {\bf v}_i}{\sum_{i=1}^{n}f_i}
\label{pas}
\end{equation}
Word vectors are acquired using  word2vec\footnote[3]{https://code.google.com/archive/p/word2vec/source/default/source} from the same raw corpus that we use for knowledge acquisition (see Section~\ref{setting}).
In our experiments, we only used grammatical function ``subj'' (subject) and ``dobj'' (direct object)
because these two grammatical functions are considered to be relatively more informative.
Then the cosine similarity between PAS vectors transformed from a PropBank sense and case frames is calculated. 
Each case frame is mapped to its most similar PropBank sense.


\section{MAIN PROBLEM OF SURFACE CASE FRAMES}
\label{srl:problem}

In this section, 
\pagebreak
we describe the fact that due to the morphological characteristics of Chinese, using Chinese surface case frames is not enough to correctly represent the semantic structure of the language. We explain the reason that applying an SRL process to extract deep case frames is indispensable.

In previous work \cite{kawahara-hlt2001,Kawahara:2006}, case frames for Japanese are composed of all the instances of noun phrases and their corresponding case markers.
For example, all the instances of ``ga'' case correspond basically to the ``subject'' of the given predicate. 
Instances of ``wo'' case are basically the ``direct object'' of the given predicate. Other cases such as ``ni'' can indicate ``location'', ``time'' or ``direction''.
During the automatic PAS extraction for Japanese, there are also ambiguous case makers that can represent multiple meanings.
The most common one, for example, is ``wa'' in Japanese. This marker always functions as the topic marker.
The ``wa''-marked argument is normally emphasized as the topic of the sentence. It can correspond to either ``ga'' case or ``wo'' case, and sometimes ``ni'' case.
To deal with such ambiguity, one can simply discard all instances of ``wa''-marked NPs to make case frames more precise.

As we have discussed in Section 1 and Section 4.2, our Chinese case frames are composed of grammatical functions (cf. \nocite{JIN14.828}Jin et al. 2014). 
Such grammatical functions include ``subject'', ``direct object'', ``indirect object'' and ``prepositional phrases''. 
In Chinese, sometimes it is difficult to assign grammatical functions (i.e. surface cases) to sentences such as the following:
\begin{itemize}
\item[(1)] 苹果 (apples) / 我 (I) / 吃了 (eaten) / 很多 (a lot).
\item[(2)] 我 (I) / 苹果 (apples) / 吃了 (eaten) / 很多 (a lot).
\item[(3)] 我 (I) / 吃了 (eaten) / 很多(a lot) / 苹果 (apples).
\item[(4)] 我 (I) / 吃了 (eaten) / 很多(a lot).
\item[(5)] 苹果 (apples) / 吃了 (eaten) / 很多 (a lot).
\end{itemize}
Sentences (1) through (3) have the same meaning: ``I have eaten a lot of apples.''
However, as we can see from the sentences, 
the word ``苹果 (apple)'', which is the direct object of ``吃了 (eaten)'', and the word ``我 (I)'' can appear in various word orders.
Also, because omissions occur frequently in Chinese, sentences (4) and (5) are also commonly used, which mean ``I have eaten a lot'' and ``(I) have eaten a lot of apples'', respectively. 
Without considering the actual meaning of ``我 (I)'' in (4) and ``苹果 (apples)'' in (5), they are labeled as ``subject'' in the surface case representations. 
If one tries to figure out which word is ``subject'' and which word is ``object'', it is always problematic because of the flexible word order and omission. 

Although two sentences have the same predicate and the same syntactic structure, NPs may be assigned different grammatical functions, as shown in (6) and (7).
\begin{itemize}
\item[(6)] 中国 (Chinese) / \UTFC{4E52}\UTFC{4E53}球 (table tennis) / \UTFC{8C01} (who) / 也 (ever) / \UTFC{8D62} (win) / 不了 (not).
\item[(7)] 中国 (Chinese) / 足球 (soccer) / \UTFC{8C01} (who) / 也 (ever) / \UTFC{8D62} (win) / 不了 (not).
\end{itemize}
Sentence (6) is interpreted as: ``Nobody can win Chinese table tennis.'' while sentence (7) means: ``Chinese soccer cannot win anybody.''
``Table tennis'' in (6) and ``soccer'' in (7) should be labeled as {\it Object} and {\it Subject} respectively even though the predicate and the syntactic structure of the sentences are identical.

In the case of Japanese, it is possible to make use of morphemes attached to the predicate to disambiguate sentences with similar structures.
For example, the following sentences are the Japanese translations for sentences (4) and (5).
\begin{itemize}
\item[(8)] 私が (I) / たくさん (a lot) / 食べた (eaten). 
\item[(9)] りんごが (apples) / たくさん (a lot) / 食べられた (eaten).
\end{itemize}
There is always an additional morpheme (e.g., ``られ'') attached to the predicate which shows the voice of the whole sentence.
In the above example, sentence (8) can be regarded as active voice and sentence (9) is in passive voice.
Unlike Japanese, which is an agglutinating language, Chinese is an isolating language, and thus lacks morhemes.
There are very few such markers that indicate the transitivity, voice and tense.
This makes it almost impossible for a system to automatically identify grammatical functions correctly.
To solve this problem, we apply an SRL process to discover a deeper level case representation.


\section{APPLYING HIGH-QUALITY DEEP CASE FRAMES TO SRL}
\label{srl:deep}

\subsection{High-quality Semantic Role Selection}

Similar to the previous work described in \citeA{Jin:2013:ijcnlp}, instead of using all the SRL outputs,
we propose to use only automatically generated semantic roles of high quality, which is shown in Figure~\ref{flow} labeled as step \textcircled{\raisebox{-0.8pt}{4}}.

In particular, the standard training section of the human-annotated data is used to
train a base SRL model (which includes three sub-models for predicate sense disambiguation (PD), argument identification (AI) and argument classification (AC)). Then, another part of the human-annotated data is used to apply SRL using the base model.
From these results, we acquire training data for semantic role selection by collecting each semantic role.
We then judge the correctness of each semantic role according to the gold standard annotations. 
All correct semantic roles are used as positive examples and the incorrect ones are used as negative examples for semantic role selection.
Judging whether an automatic semantic role is reliable can be regarded as a binary classification problem.
We use SVMs to solve this problem. We use the feature set for SRL described in Jin, Kawahara, and Kurohashi (2015)\nocite{jin-kawahara-kurohashi:2015:SIGHAN-8} as basic features. 
It contains predicate features that are extracted from the target predicate; argument features which are extracted from each candidate argument.
Additionally, the automatic semantic labels produced from the base SRL system are used as another feature.
We also extract the same features described in Table~\ref{table:surfaceFeature} using surface case frames, which have a positive effect on SRL.


\subsection{High-quality Deep Case Frame Construction}

Due to the major issues described in Section~\ref{srl:problem}, 
case frames constructed using surface cases may be problematic.
For example, for the predicate ``吃 (eat)'', both the arguments ``苹果 (apple)'' and ``我 (I)'' are assigned to the same surface case ``subject''.
If one tries to use this kind of surface case knowledge for tasks that require semantic information, such as machine translation (MT),
it may lead to a performance drop.
So we propose to construct deep case frames that are relatively more representative than the surface case frames.
By the deep case, we mean using the semantic roles acquired from large-scale raw corpus for case frame construction.
This step is presented in Figure~\ref{flow} as step \textcircled{\raisebox{-0.8pt}{5}}.

For predicate identification (PI) step, we simply regard every word with a POS tag beginning with ``V'' as a predicate.
The PD step in the SRL pipeline assigns a PropBank sense ID to each predicate.
This is equivalent to the unsupervised clustering for surface case frames and thus no additional clustering step is required.
After argument identification and argument classification, we only use these semantic roles with high reliability.
For each predicate with different PropBank sense IDs, we collect all the high-quality semantic roles to compose the deep case frames.


\subsection{Using High-quality Deep Case Frames for SRL}

Syntactic information such as dependencies is essential for SRL.
In Section~\ref{srl:surface}, we used surface case frames to provide additional knowledge, namely that of grammatical functions, 
for an SRL system and gained a slight improvement as will be shown in Section~\ref{srl:exp}. 
Deep case frames are compiled using automatic semantic roles that use semantic-level representation.
Thus, we consider that using deep case frames as additional knowledge 
has a more direct impact on the performance on SRL. 
Similar to the method described in Section~\ref{srl:surface},
we also propose a set of features extracted from deep case frames which are listed in Table~\ref{table:deepFeature}.
The first four features do not concern the predicate sense.
These features are similar to the predicate-argument pair features described in Section~\ref{srl:surface}.
The last four features are similar to the case frame features described in Section~\ref{srl:surface}.
However, because the deep case frame ID is identical to the PropBank ID, no mapping processes are needed.

\begin{table}[t]
\caption{Deep case features for SRL}
\label{table:deepFeature}
\input{02table05.tex}
\end{table}


\section{EXPERIMENTS}
\label{srl:exp}

\subsection{Experimental Settings}
\label{setting}

For large-scale knowledge acquisition, 40 million sentences from Chinese Gigaword 5.0 
\linebreak
(LDC2011T13)\footnote[4]{We only used sentences written in simplified characters in Chinese Gigaword.} were used.
For the high-quality dependency selection approach in the knowledge construction pipeline, the Stanford parser was used to apply dependency parsing.
The training section of Chinese Treebank 7.0 was used to train the dependency parser and the official development section was used to train a classifier for high-quality dependency selection. Using the official evaluation section of CTB 7.0, we evaluated the quality of those selected dependencies using unlabeled attachment score (UAS), which calculates the percentage of correctly identified dependency heads. Since that the mechanism of producing dependency labels using the Stanford parser is a black box, and since there does not exist such golden data for dependency label evaluation, labeled attachment score (LAS) was not concerned in this study.

During the CRP approach for surface case frames compilation, there are two hyper-paramters $\alpha$ and $\beta$. $\alpha$ controls how likely a new case frame is to be created, while $\beta$ is a hyper-parameter for dirichlet distribution which controls the peakedness. We set both hyper-parameters as 1.

For SRL, we used the Chinese section of CoNLL-2009 shared task data (we substituted the syntactic dependencies and dependency labels produced by the Stanford parser).
Althought the original data set uses Maltparser style dependency labels, to be consistent with the dependency labels used in the addtional knowledge, we chose to ultilize the Stanford dependency labels.
Automatically obtained morphological and syntactic information (the columns begin with ``P'') was used.
PD and AI, AC step are regarded as multi-class classification problems.
We employed OPAL\footnote[5]{http://www.tkl.iis.u-tokyo.ac.jp/{\textasciitilde}ynaga/opal/} to solve these problems.
We set the options as follows: polynomial kernel with degree 2; passive aggressive I learner; 20 iterations.
The base SRL system without using additional knowledge was used as a baseline.
To examine the effect of different quality of knowledge, we used different set of PASs which were extracted under different dependency selection thresholds (20\%, 50\%, and 100\%). The official script provided on the CoNLL-2009 shared task website was used for evaluation.

For semantic role selection, similar to dependency selection, the training section of CoNLL-2009 shared task data was used to train the base SRL model.
The development section in CoNLL-2009 shared task data was used to apply automatic SRL and obtain training data for the semantic role selector. 
We evaluated the semantic role selection approach by calculating the percentage of correctly judged semantic roles (predicate senses are not counted).
For deep case frame construction, we used the Stanford parser for syntactic analysis. The base SRL system was used to assign semantic roles. We applied the proposed framework to 40 million sentences from Chinese Gigaword 5.0. We constructed deep case frames of different quality (20\%, 50\%, and 100\%) to extract extra features to support the base SRL system.

\begin{figure}[b]
\begin{center}
\includegraphics{25-2ia2f3.eps}
\end{center}
\caption{Precision-recall curve of dependency selection (left) \& semantic role selection (right)}
\label{sr-pr}
\end{figure}


\subsection{Experimental Results}
\label{srl:result}

Figure~\ref{sr-pr} shows the precision-recall curves of dependency selection and semantic role selection.
For dependency selection, we achieved a precision over 90\% when lowering the recall to around 20\%.
For semantic role selection, using additional surface case frame features gains a slight improvement compared to the basic features.
Table~\ref{table:num} shows the number of P-A pairs we acquired for both surface and deep case frames.
We used three different threholds to filter automatic parses. N\% means using the threhold which selects top n\% of the automatic parses from the evaluation data for dependency selection or semantic role selection step. `100\%' means that the selection step was not applied. 

\begin{table}[b]
\caption{Number of P-A pairs acquired (million)}
\label{table:num}
\input{02table06.tex}
\end{table}
\begin{table}[b]
\caption{Evaluation results of Chinese SRL using surface and deep case frames}
\label{table:surface}
\input{02table07.tex}
\vspace{4pt}\small
The * mark means that the result is regarded as significant (with a $p\ \text{value} < 0.01$) using McNemar's test.
\end{table}

Table~\ref{table:surface} shows the experimental results of SRL using surface and deep case frames as additional features.
Knowledge (n\%) indicates that the corresponding selection standard is used to filter automatically extracted knowledge as described in Table~\ref{table:num}.
The baseline system achieved an F-value of 76.63\%. When using surface case frames, only using the selection standard of 20\% gained an improvement with statistical significance.
For deep case frames, using all three selection standards gained an improvement with statistical significance. A larger improvement was gained as the quality of deep case frames increases.
It is worth pointing out that when using the baseline method, we achieved an F-value of around 79.4\% on CoNLL-2009 shared task original data set (where the dependency labels follow the MaltParser style, which is different from the Stanford dependencies). 
This result has outperformed the best system for Chinese SRL in CoNLL-2009 shared task, which was 78.60\%. 
The improvement is considered to be gained due to fine tuning of baseline features.
When applying the baseline system on the substituted version of dataset for dependency label consistency with the additional knowledge, the baseline F-value drops to 76.63\% mainly because we trained using automatic dependency parses instead of golden parses. 
As we can see from the results, using deep case frames gained more improvements than using surface case frames.
This is because deep case frames are able to directly provide semantic-level information that is insufficient in the training data of the base SRL system.
Furthermore, the results show that the high-quality semantic role selection approach has a positive effect on SRL.


\subsection{Discussion}
\label{srl:discussion}

Using dependency parses and semantic roles produced from automatic base parsers as additional knowledge will certainly be noisy.
Considering reducing the bad influence caused by the noise, we proposed to select dependencies from automatic parses with high quality.
By employing binary classifiers we succeed in determining whether each dependency is reliable. 
From the results shown in Figure~\ref{sr-pr}, we observed a promising precision-recall curve which suggests that the binary classifier trained using auto-collected training data is of a very high quality. By reducing the recall value we can obtain a precision value greater than 95\%. 
A similar approach is also applied for picking those high-quality semantic roles from the results of the baseline SRL system. The results showed that using surface case frames as additional features also slightly improve the semantic role selection approach. Also, semantic role selection achieved a performance that is equivalent to dependency selection.

By using the selection approach, there is inevitably a trade-off between the quality and information variety for the additional knowledge. 
By reducing the recall value, even though we can acquire knowledge with higher quality, the amount of information contained in the knowledge will decrease.
There is no standard to decide a proper number of the recall value for knowledge compilation. 
In the experimental settings, we first utilized the whole noisy knowledge to examine the efficacy. 
Then we picked the number of 50\% and 20\% roughly to represent medium quality and high quality.
According to the experimental results, using higher-quality knowledge gained a larger improvement. 
However, it is still unknown whether the performance of SRL system will drop if we keep increasing the threshold for dependency and semantic role selection (i.e., reducing the recall value). Finding the optimal balance point for quality and variety to achieve the best performance for an SRL system remains an important component of our future work.

When judging the semantic relations between a predicate and its arguments, the baseline model highly relies on automatic syntactic parses.
Using extra features extracted from high-quality case frames essentially compensates for the lack of lexical information that the baseline model can learn from training data.
In some sense, it also reduces the effect of erroneous automatic syntactic parses. Take the following sentence as an example.
\begin{itemize}
\item[(10)] \UTFC{97E9}国 (Korea) / 在 (in) / \UTFC{534E} (China) / 投\UTFC{8D44} / (invest) / 企\UTFC{4E1A} (enterprise) / \UTFC{603B}数 (summation) / \UTFC{4E3A} (is) / 五千八百八十三 (5883) / 家 ([QUANTIFIER]).
\item[] (The total number of enterprises that Korea invested in China is 5883.)
\end{itemize}
When the dependency parser failed to correctly label ``\UTFC{97E9}国 (Korea)'' as a child node of the predicate ``投\UTFC{8D44} (invest)'', a base model also failed to recognize ``\UTFC{97E9}国 (Korea)'' as a semantically related argument of ``投\UTFC{8D44} (invest)''. 
However, by exploiting additional knowledge, the proposed system successfully identified ``\UTFC{97E9}国 (Korea)'' as a semantic argument of ``投\UTFC{8D44} (invest)'' and also correctly assigned the semantic role ``A0'' to it. We also show another example below.
\begin{itemize}
\item[(11)] 重大 (significant) / 科技 (scientific) / 成果 (achievement) / 迅速 (rapidly) / \UTFC{8F6C}化 (convert) / \UTFC{4E3A} (to) / \UTFC{73B0}\UTFC{5B9E} (practical) / 生\UTFC{4EA7}力 (productivity). 
\item[] (Significant scientific achievement has been rapidly converted to practical productivity.)
\end{itemize}
We found that even when the dependency parser correctly assigned ``成果 (achievement)'' as the ``subject'' of the predicate ``\UTFC{8F6C}化 (convert)'', a base model still incorrectly assigned a semantic role ``A0'' to ``成果 (achievement)''. 
This is normally a difficult case for an SRL system to identify a preceding ``subject'' as ``A1'' especially when the passive voice expression is missing. 
By accessing additional knowledge with wider coverage, we observed that the proposed system successfully assigned ``A1'' instead of ``A0'' to the subject ``成果 (achievement)''.


\section{CONCLUSION \& FUTURE WORK}
\label{srl:conclusion}

We proposed a method for using additional knowledge to improve Chinese SRL.
To solve the case ambiguity problem in the surface case representation, especially for Chinese,
we utilized automatic semantic roles produced by an SRL system for a better representation.
The experimental results showed a promising result for high-quality semantic role selection.
Also, using high-quality deep case frames that are composed of semantic roles can significantly improve the baseline SRL system.

In the future, we plan to make use of other low-level knowledge sources such as word embeddings \cite{Ronan:2011} and word clusters \cite{koo-carreras-collins:2008:ACLMain}
to improve dependency parsing and SRL. The recent SRL approaches are mostly point-wise. Features are extracted from only pairs of the predicate and an argument candidate. We plan to design a higher-order system to capture more global features following the idea of higher-order dependency parsing.
Also, reranking is widely utilized in many SRL systems and we plan to combine our surface/deep case knowledge with a reranker in order to further improve Chinese SRL.


\acknowledgment

The authors would like to thank Raj Dabre who gave a lot of help with English writing check for this article.

A part of this work was presented at the 15th Conference of the European Chapter of the Association for Computational Linguistics.



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\begin{biography}

\bioauthor[:]{\bf Gongye Jin}{
Gongye Jin received the M.S. and Ph.D. in Informatics from \mbox{Kyoto}
University in 2012 and 2016. He is currently a senior system engineer of IT
engineer at Language Processing Engineering Division, R\&D Headquarters,
Canon IT Solutions Inc. His research interests include natural language
processing, knowledge acquisition, and semantic role labeling.
}

\bioauthor[:]{\bf Daisuke Kawahara}{
Daisuke Kawahara received his B.S. and M.S. in Electronic Science and
Engineering from Kyoto University in 1997 and 1999, respectively. He
obtained his Ph.D. in Informatics from Kyoto University in 2005. He is
currently an associate professor at the Graduate School of Informatics
at \mbox{Kyoto} University. His research interests center on natural language
processing, particularly knowledge acquisition and text understanding.
}

\bioauthor[:]{\bf Sadao Kurohashi}{
Sadao Kurohashi received the B.S., M.S., and Ph.D. in \mbox{Electrical} Engineering
from Kyoto University in 1989, 1991 and 1994,
respectively. He has been a visiting researcher of IRCS, University
of Pennsylvania in 1994. He is currently a professor of the Graduate
School of Informatics at Kyoto University. His research interests
include natural language processing, knowledge
acquisition/representation, and information retrieval. He received
the 10th anniversary best paper award from Journal of Natural Language
\mbox{Processing} in 2004, 2009 Funai IT promotion award, and 2009 IBM
faculty award.
}

\end{biography}



\biodate





\end{document}
