Proceedings of NTCIR-8 Workshop Meeting, June 15–18, 2010, Tokyo, Japan

WHU Question Answering System
at NTCIR-8 ACLIA Task
Han RenDonghong Ji

Jing Wan

School of Computer,Wuhan
University
129 Luoyu St., Wuhan, China

School of Computer, Wuhan
University
129 Luoyu St., Wuhan, China

Center for Study of Language &Information, Wuhan University
129 Luoyu St., Wuhan, China

cslotus@mail.whu.edu.cn

donghong_ji@yahoo.com

jennifer_wanj@yahoo.com

ABSTRACTIn this paper, we describe our system implemented for the
NTCIR-8 CCLQA task. The system consists of a questiontranslation model and a general question answering system forboth factoid and complex questions. The translation model
combines a translation engine and an online dictionary, which canprovide more accurate translations of named entities in thequestions. With regard to the question answering system, a PLSA
based approach is introduced for answer sentence acquisition. For
answer ranking, our system expands the question set bysummarizing relevant sentences from a web knowledge base andleverages both semantic and statistical information of questions.
In the official evaluation results, our system achieves 18.41% Fscore in English to Chinese subtask and 25.66% in monolingual
Chinese subtask.

shows a general performance, which can deal with documents ofany other language.
The rest of the paper is organized as follows. In Section 2 thesystem architecture is given. Section 3 gives the description ofeach model in detail. In Section 4, we give the experimental
results and error analysis. Finally, the conclusion and future work
are shown in Section 5.

2. System Architecture
The processing mechanism of our system is shown in Figure 1,
which contains five main models: question translation, document
retrieval, answer sentence acquisition, answer ranking and nugget
extraction. The system carries out a pipeline approach with thefollowing processes:
1) the questions are matched with the patterns to identify theirtypes;

Keywords
Cross-Lingual Question Answering, Complex Question,
Document Retrieval, Probabilistic Latent Semantic Analysis,
Answer Ranking2) the key terms in the questions are extracted and translated, andthen are submitted to the document retrieval model;

1. INTRODUCTION

3) for each sentence in retrieval results, the answer sentenceacquisition model is utilized to compute the similarity with thequestion; here we expand the question set by summarizing
relevant sentences from a web knowledge;

The previous participation of WHU at CCLQA task focused on
monolingual Chinese complex QA[7], in which patterns by
manual work are adopted to acquire answer candidates. In
NTCIR-8, complex questions are taxonomically extended as five
types and factoid questions are also introduced, aiming at
evaluating more effective QA systems. Since patterns are
insufficient to satisfy the information requirements of questions
with extended types, the system with less patterns or rules should
be considered for a general performance.
Different with the previous system, we introduce a PLSA based
answer sentence acquisition model to extract sentences that may
contain the answers. By mapping sentences to a latent semantic
space, sentences that semantically relevant with questions are
selected and ranked as answer candidates. We also consider
linguistic information in answer ranking and employ the web
knowledge to expand the questions.
This year we participate in cross-lingual Chinese QA task. For
this purpose, a translation model is implemented and EN-CS runs
are submitted as well as CS-CS ones. In addition, we align our
retrieval model with other IR4QA systems by submitting IR runs,
aiming at evaluating the performance of our retrieval model.
Although the retrieval documents are monolingual, our approach4) after that, answer candidates are ranked by weighting theirsemantic and statistical similarity;5) finally, the top 30 answers are selected to extract nuggets.
Details of each model are described in the following section.

3. Cross-Lingual QA System
In this section, we describe the pipeline approach, including
question analysis, document retrieval and answer extraction andranking, in our cross-lingual system. The monolingual system isalmost same with the cross-lingual one except for the question
analysis model.

3.1 Question Analysis
The goal of the question analysis is to determine the question type
and to find key terms of a question. Translation model is also
introduced for E-C task.

3.1.1 Question Type Identification
In NTCIR-8, not only complex question types are extended, butalso factoid questions are added in the question set. Even we are― 31 ―

Proceedings of NTCIR-8 Workshop Meeting, June 15–18, 2010, Tokyo, Japan

Figure 1. QA system processing mechanism

meant to follow the matching method introduced in our previous
system, there are still some patterns that could not be utilized for
identification because of multi-matching. For instance, in ourprevious system, the pattern that a question starts with ‘Who be’
exclusively matches as ‘BIOGRAPHY’ type, whereas it could
match another type of questions provided this year, i.e., thefactoid questions asking for persons’ names. We refine the patternlist and modify some heuristic rules so that the matching method
can fit for the task of this year. Table 1 shows the pattern list.
Note that the questions should be firstly dealt with POS tagging
and Named Entity Recognition before matching.
Table 1. Question type pattern list
type

pattern

DEFINITION

չՇਢ/ਢչՇ/What is

BIOGRAPHY

B

B

B

RELATIONSHIP
EVENT
WHY
PERSONORGANIZATIONLOCATIONDATEFor Chinese questions, we utilize ICTCLAS 1 , a free Chinese
lexical analysis tool that contains Word Segmentation, POS
tagging and NER(Named Entity Recognition), to annotate them.
Then all words and phrases except interrogation words andfunctional words are selected as key terms.
TPF

FPT

For English questions, the LBJ Tools 2 , a free English linguistic
analysis tool that contains POS Tagging, NE Chunking and NER,is utilized to annotate them. Although each model in it isindependent, the results of POS Tagging have general format sothat we can easily use them in the other two models. We also use
a standard Porter algorithm for word stemming. Finally, NE
chunks, named entities and other stemmed words except
interrogation and functional ones are extracted as key terms.
TPF

FPT

3.1.3 Key Term Translation

冓ਢ+NE PER /ਢ冓+NE PER /Who is+NE PER
ࡉ+ऱ䤤ߓਢչՇ
the relationship/
interrelationship between٨䢡/䢡‫נ‬/٨‫נ‬/円‫נ‬/ୌࠄ
B

3.1.2 Key Term Extraction

B

We are not meant to translate the full text of each question,
because our retrieval model treats each query as a bag of words,
not considering the context of each word or the syntactic structureof each question. Initially, we employ some translation engines,
i.e., Google Translate 3 and Yahoo Fanyi 4 , but there are stillsome key terms that can not be translated correctly. For instance,
the name of a movie is ‘Riding Alone for Thousands of Miles’(ACLIA2-CS-0002) which means ‘ Տ ߺ ߨ 㧧唿 ’, whereas thetranslation result is ‘㧧唿Տߺߨ’ in Google Translate and ‘㧧㭦ଊ݄䀀‫א‬ᩉ儳ऱ૎ߺ’ in Yahoo Fanyi. Some named entities arealso mistranslated such as ‘呇࢘ႂ’ as the result of ‘Sean Chen’.
Alternately, we employ an online dictionary, ICIBA 5 , to translate
chunks and named entities. Although it is also based on statisticaltranslation, the dictionary can give correct results of most chunks
and named entities. Experimental results in the following section
will show that the cross-lingual retrieval results that generated by
the model that employs the online dictionary is close to the

B

TPF

䢠չՇ
Why/What is the reason
冓+vp-NEWho+vp-NEୌ㠺/ࠄ+᫕Ე/ຝ吋Whichୌߺ/ୌ㠺‫ֱچ‬/ୌࠄ‫ֱچ‬Where/Which+place۶㦍/չՇ㦍ଢ/ୌԫ‫ڣ‬
When/What time/Which year

FPT

TPF

FPT

TPF

Even the patterns can match most of questions accurately, there
are still some multi-matching questions. We give some heuristic
rules to get over the situation:

1
TP

PT

2

1) a longer pattern has a higher use priority than a shorter one if
there is more than one pattern that matches a question;

TP

PT

3
TP

PT

4

2) if a question is matched by a pattern, it should not be matchedwith other patterns.

TP

PT

5
TP

PT

― 32 ―

http://ictclas.org
http://l2r.cs.uiuc.edu/~cogcomp/http://translate.google.com/
http://fanyi.cn.yahoo.com/
http://fy.iciba.com/

FPTProceedings of NTCIR-8 Workshop Meeting, June 15–18, 2010, Tokyo, Japan

monolingual retrieval results. For other key terms, we still use
Google Translate to deal with them.

languages for document relevance estimation[2][3]. In our system,
we employ PLSA model as the answer sentence retrieval model.
Following is the description of the PLSA model in our system.

3.2 Document Retrieval

Given a sentence set S, a term set W and a topic set Z, theconditional probability of sentence-term P(s, w) can be describedas follows:

Passage retrieval seems to be identified with most systems in
CCLQA task, we still consider documents as the retrieval units.
Our reason lies in two folds: 1) most documents are composed ofshort passages; some passages have only one sentence, and somepassages even have several words, not a sentence. These passages
may not be considered in passage retrieval in most cases or theirranks are very low, whereas some of them are potentially relevantto the questions. 2) our PLSA based answer sentence acquisition
model described in the following section need enoughsemantically potentially relevant words to build the model,
whereas passage retrieval could filter many of them. Based on theconsiderations above, we utilize documents as our retrieval results.
Table 2. Experimental results in NTCIR-7 dataset
methodbigram+unigram+
BM25
bigram+unigram+
VSM

M-AP

M-Q

M-nDCG

0.60610.61920.79560.5016

0.48730.6439unigram+VSM

0.38970.3726

0.5292

P s, w

P s ¦P w| z P z | s

(1)

zZ

In (1), P(w | z) represents the conditional probability of words inlatent semantic layers(or topics), P(z | s) represents the conditional
probability of topics in sentences. Here the count for topic set Z is
between 20 and 100. Then the model fits with the EM algorithm
and export the optimal P(Z), P(W | Z) and P(Z | S). When a newquery is coming, it is projected to the topic space Z by using EM
algorithm. The similarity of the query and each sentence can beacquired by computing the similarity of the probabilistic
distribution between them in the topic space.
Our algorithm is described as follows. First, initialize the P(s, w)
for each sentence s  ^s1 , s2 ,..., sn ` in the retrieved documents

Our retrieval model is based on Lucene, a free retrieval
framework, and the index units are unigram and bigram. The
original retrieval model in Lucene is VSM, whereas we find thatthe BM25 model that we employed in our system outperforms it
in document retrieval. Table 2 shows the results of document
retrieval in NTCIR-7 dataset. We can see from the table that
BM25 model integrated with unigram and bigram promote theperformance substantially.

3.3 Answer Sentence Acquisition
Pattern matching method is utilized in our pervious system foranswer sentence acquisition. However, some patterns are out ofdate for the test set, and new patterns are more difficult to fetch
since five more types of questions are added in this year. In
addition, for a general performance, the answer sentenceacquisition for factoid and complex questions should not be
divided into two parts. Hence an acquisition model without handcrafted rules should be considered, and it should have the ability
to perform various types of questions.
For the complex questions, the potential answers mostly indicate
the complex semantic relations with them so that the bag-of-wordmodels are insufficient to compute the similarity between theanswers and the questions. To solve it, some systems[1][6]employ LSA based models to build a semantic layer; thus thesimilarity between documents and words, or documents anddocuments are able to compute by using the semantically latent
relations. However, the meaning of the decomposition algorithm
is indefinite so that the model could lead to an uncontrolled
performance for retrieval. For a clear decomposition meaning,
PLSA(Probabilistic Latent Semantic Analysis) model[4] isintroduced to build a semantic space of underlying topics, inwhich words and documents can be mapped as vectors. Some QA
systems utilize PLSA to improve answer validation, i.e., modelingby using the ratio of the frequency of w in s and in the sentence
set; and P(w | z) for each word and P(z | s) for each sentence areiteratively computed by EM algorithm. Then the query built fromthe question is mapped into the topic space to compute P( z | q )by using EM algorithm, keeping P(w | z) invariably. After that,the conditional probability P( w | q) and P( w | s ) for eachsentence in the retrieved documents is computed according to theformula (2):¦ P(w | z ) P( z | s)P(w | s)(2)zZ

Finally, a cosine similarity method is utilized to compute thesimilarity between the query q and each sentence in retrieved
documents, and the sentences that the weighting values are abovea threshold are selected as the answer sentences.

¦ P w| s P w| sSim( s1 , s2 )1wW

¦ P w| s

wW

1

2

2

¦ P w| s

wW

2

(3)

2

For a better performance, we submit each question to Wikipedia
and summarizing the results as extended query sentences. The
summarization approach is described in [9], which is suitable forsummarizing in Wikipedia. Then we compute the similarity
between each sentence in retrieved documents and query sentence
set. Sentences that their similarities are above the threshold are
selected into the answer set.

3.4 Answer Ranking
For a better performance of answer validation and ranking, someapproaches combine various resources of evidence, but most of
the resources are language-dependent and may not fit for complex
questions. For a general performance, our method of answer
ranking considers two main features: shallow semantic andstatistical information of sentences. The motivation is, a sentence
can be a potential answer candidate if 1) the sentence and thequestion have a semantically similar relation; and 2) most of the― 33 ―

Proceedings of NTCIR-8 Workshop Meeting, June 15–18, 2010, Tokyo, Japanwords in the sentence are also appear in questions or sentences
derived from web knowledge bases. More specifically, although
the retrieved sentences have the latent semantic similarity withquestions, we should still consider the long distance dependency
relations that could probably result in a low score for an answer
candidate. On the other hand, a statistical similarity that treats thesentences as a bag of words could probably balance the impact of
the semantic bias.
For semantic similarity, we consider the similarity of the main
semantic roles that primarily profile the features of sentences. We
choose the verb based labeling architecture derived from
PropBank, in which ‘predicate’, ‘subject’, ‘object’ and somemodifiers are the core roles for a sentence. For each sentence inanswer candidate set we only label PRED, A0-A4, AM-LOC and
AM-TMP and combine each predicate and the correspondingargument to a pair. The structure of a pair is described as follows:{w 1 | PRED, w 2 | AM}
If a pair lies in both a sentence and a question, it is viewed as amatched pair. More specifically, every term and its semantic
dependency relation should be matched if the pair is matched.
Following is the weighting formula that we compute the semantic
similarity:
B

B

B

B

# of matched pairsmax{# of pairs in s, # of pairs in qi }M ( s, qi )(4)

For labeling of semantic roles, we utilize a system that we
proposed in [8] to extract pairs. The system handles syntactic
dependency parsing with a transition-based approach and utilizes
MaltParser 6 as the base model. The system also utilizes a
Maximum Entropy model to identify predicate senses andclassifies arguments.
For statistical similarity, we simply utilize a cosine similarity tocompute it. The weighting formula of our method for answeringranking is as follows:
TPF

ws

FPT

max{D < M ( s, qi )  (1  D )<CosSim( s, qi )}(5)Į is an adjusting parameter, and q denotes each sentence in thequery sentence set described in section 3.3. According to (5), thesentence that mostly similar to a query is acquired. Finally, 30
answer sentences is extracted and ranked by their ranking scores.

3.5 Nugget Extraction
Our nugget extraction model is simple. For factoid questions, we
extract named entities according to the type of them. For instance,
if a question is classified as the type of ‘PERSON’, we extract theperson names from the ranked answer sentences and rank themwith the scores of the sentences. For complex questions, we
preserve the sentences as the answers.

We submit three runs for this subtask. E-C-01-T and C-C-02-T
utilize the exact methods given in section 3.1. E-C-03-T is very
close to E-C-01-T except that multiple translation results using
the online dictionary is added as the key terms. The motivation isthat since a term probably has the multiple translation results,
they should be reserved for retrieving more relevant documents,
such as the name ‘Bin Laden’(ACLIA2-CS-0006), which can betranslated as ‘‫ ’࿆ࢮء‬or ‘䪪ࢮ࿆’. IR4QA results indicate that EC-03-T achieves a better performance than E-C-01-T. We alsofind that the latter translation result of the name seldom appears at
the documents. Hence the frequency should be considered whenselecting multiple translation results of a word. In addition, somewords are not translated correctly, such as ‘screen’(ACLIA2-CS0002), which is mistranslated as ‘ ጉ 㤤 ’, and that’s why the
retrieval results using E-C translation runs achieve a lower
performance than using C-C runs. It is also notable that theperformance of question type identification in E-C run
outperforms that of in C-C run. For instance, the question
ACLIA2-CS-0006 in English is easy to identify because itmatches a pattern of the type ‘RELATIONSHIP’ in Table 1,
whereas the corresponding Chinese question can not matches any
pattern of this type. It mainly because that patterns in some types
are inaccurate. Moreover, some patterns probably lead to overmatching. But most of them are still appropriate in our system.
Hence the patterns should be refined for a better identification
performance.

4.2 IR4QA Runs
Retrieval model may impact the performance of the QA system.
To investigate the appropriate IR strategy that brings out thesuperior QA performance, CCLQA participants are also required
to submit their retrieval results with IR4QA participants. Due to
27 invalid topics in which the number of relevant documents
found in the depth-100 pool was fewer than five, only 73 topics
are utilized in the evaluation set.
In this year, we submit 2 runs for E-C and C-C task respectively.
For E-C runs, we investigate the impact of two translation
strategies to retrieval results. Run 1 utilizes both the translationengine and the online dictionary mentioned in section 3.1.3. Run
2 only utilizes the translation engine. For C-C runs, different
retrieval units and models that impact the results are checked. Run
1 utilizes both unigram and bigram as the retrieval units, and run
2 just utilizes bigram. The evaluation results for all runs areshown in Table 3.
Table 3. The official IR4QA results of oursystem(before bug fix)

4. Experimental Results and Analysis
We submit three types of runs in this year’s task: question
analysis, IR4QA and CCLQA. Because of time limitation, we do
not participate in the IR4QA+CCLQA subtask.

4.1 Question Analysis Runs

6
TP

PT

M-AP

rank

M-Q

rank

M-nDCG

rank

E-C-01-T 0.371
E-C-02-T 0.3555
C-C-01-T 0.4128
C-C-02-T 0.4055

15
16
5
8

0.40850.393
0.45280.445815
16
4
80.61390.59890.66290.657714
17
4
6

Ranks in Table 3 are based on all runs for IR4QA task. Actually,in E-C subtask, our best run achieves the rank 5 of M-AP, 5 of MQ and 4 of M-nDCG; in C-C subtask, our best run achieves therank 4 of M-AP, 4 of M-Q and 4 of M-nDCG. The ranks provethat our retrieval model is effective to some extent. From the offi-

http://w3.msi.vxu.se/~jha/maltparser/― 34 ―

Proceedings of NTCIR-8 Workshop Meeting, June 15–18, 2010, Tokyo, Japan

result is smallest 7 . It indicates that our translation strategy
outperforms the other participants in some extent.

Table 4. The official CCLQA results of our system
EN-CS
Definition
Biography
Relationship
Event
Why
PersonOrganization
Location
Date
Overall

Recall0.19670.52380.19170.31130.06710.80000.60000.80000.40000.3161

Precision
0.22280.06780.05030.04010.00720.12000.32110.37800.16010.0775F-score0.10330.29670.10100.15570.02180.44350.54370.48490.29520.1841

CS-CS
Definition
Biography
Relationship
Event
Why
PersonOrganization
Location
Date
Overall

Recall0.38020.91670.15830.38550.10771.00000.60000.60000.80000.4100Precision
0.03800.12230.09240.03670.00920.26320.29780.35010.1893
0.0987F-score0.18850.53130.12390.18070.04180.77310.54070.41450.57950.2566TPF

4.3 CCLQA Rums
Since the human evaluation of CCLQA considers the first priority
runs submitted by participants, we only submit two runs for thistask, based on the RUN-1(tagged as 01-T) of question analysis
and IR4QA. Table 4 shows the official results for each type ofquestions by Recall, Precision and F(£=3) score.
For C-C task, the F-score over all questions is 25.66%, ranking
second in all submitted runs[5]. For E-C task, we rank first as theF-score 18.41%, because no runs are submitted for evaluation
except us.
From the official CCLQA results, some conclusions can be drawn:

The F-scores of the factoid questions greatly outperforms
that of the complex questions in both E-C and C-C task.
Although the factoid and complex questions are not
performed respectively in the retrieval and answer
acquisition model, our system is still very available forfactoid ones. It indicates that these two models in oursystem have a general performance for these two kinds ofquestions.



Our system achieves a very low performance for thequestion of the type ‘Why’ in both E-C and C-C task. It ismainly because that the semantic relations in thesequestions are more complex than others. Actually, mostanswers have the logic relations with the question, rather
than synonyms or shallow semantic ones. For instance,
when asking ‘Why is "ShenZhou" spacecraft launched inrelatively cold season’, the answer is most like a reason
chain: the spacecraft is recovered by the survey vessels; thesurvey vessels are mainly located in the southernhemisphere; the recovery task befits in summer; the climate
is quite opposite between the southern and the northernhemisphere. However, most words in the answer do notappear at the question. Alternately, sentences in answers
have logical(or casual) relations that should be inferredfrom one to another. The answer to the question can beacquired only if all the relative events are chained throughinference. Hence the inference models should be considered
for a better performance of ‘Why’ questions.



As a close view to the results we find that, in most cases
the Recall scores greatly outperform the Precision ones foreach type of the questions. And the reason is easy to know:
the nuggets extracted are too long in comparison with thehuman results. In addition, the scores of the factoid
questions greatly outperform that of the complex ones, andthis is mainly because the short answers(mostly are namedentities) are extracted as the nuggets for the factoid
questions whereas most answer sentences are remained as
the nuggets for complex questions. Hence if the system isrequired for a better performance, the nugget extraction

cial IR4QA results, we also find some situations that need to be
discussed.
FPT

The improvement of performance(1.55% of M-AP, 1.55%
of M-Q, 1.5% of M-nDCG) from E-C-02-T to E-C-01-T isderived from the utilization of the online dictionary
mentioned in section 3.1.3. On the other hand, although thetranslation engines mistranslate some terms, the retrieval
model still return most correct documents that include theanswer sentences. For instance, we utilize the different
translation results of ‘Riding Alone for Thousands of Miles’
by translation engines and online dictionaries to retrieve
documents, and most of documents, especially correct ones
within two retrieval results, are matched. In other words,
the word order in long terms does not play a very importantrole if the key words are correctly translated. Therefore, themain effect of online dictionaries is to translate some key
terms or named entities, i.e., the person names and thelocation names.



As to C-C subtask, we investigate the impact of different
retrieval units and parameters in BM25 model to the results.
It is notable that the impact for the performance isslight(0.73% of M-AP, 0.7% of M-Q, 0.52% of M-nDCG)
since C-C-01-T adopts unigram and bigram, and C-C-02-T
only utilizes bigram as the retrieval units. It implicates that
only using bigram can achieves a good performance.



In comparison with other participants that submit both the
E-C and the C-C results, we find that the positive
divergences of the performances between our E-C and C-C
7
TP

PT

We consider the divergence of the performance between E-C
and C-C results by using a score, i.e., M-AP, in C-C results tominus the corresponding score in E-C results. If the score isgreater than zero, it is named positive.

― 35 ―

Proceedings of NTCIR-8 Workshop Meeting, June 15–18, 2010, Tokyo, Japanmodel should be improved to acquire more fine-gained
answers.

5. Conclusion
In this paper, we describe our system implemented for NTCIR-8
CCLQA task. The main contribution to the system lies in two
folds: 1) a PLSA model based answer acquisition approach is
introduced for both factoid and complex questions; and 2)
semantic and statistical information are combined in the answer
ranking model for a better performance. Although a general
performance is achieved according to the official evaluation
results, we still face two important problems: 1) with the
improvement of the complexity of questions, deep knowledge inthem should be acquired by some inference mechanisms; 2) morerefined nugget extraction model are needed to improve theprecision of the system.

6. ACKNOWLEDGMENTS
This work is supported by Natural Science Foundation ofChina(Grant Nos. 60773011, 90820005), Wuhan University 985
Project: Language Technology and Contemporary Social
Development(Grant No.985yk004) and Self-research program for
Doctoral Candidates of Wuhan University in 2008.

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