Survey based on users keywords which depends on

 

 

 

Survey on Community Question Answering
System to solve the Lexical Gap

B.Deeppikaa1, A.Geetha2,
S.Sri Heera3

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1PG Student/CSE

Easwari
Engineering College, Tamil Nadu, Chennai.

2Assistant  Professor/CSE

Easwari Engineering College, Tamil Nadu, Chennai.

3Assistant Professor/CSE

Easwari Engineering College, Tamil Nadu, Chennai.

 

 

Abstract: Web search engines give a ranked list of
related documents based on users keywords which depends on various aspects like
popularity measures, keyword match, frequency of accessing documents in which
users have to check every specific document for getting the desired information
and it causes information retrieval a prolonged process. Community Question
Answering (CQA) system focus to deliver users short and precise answers instead
of irrelevant documents. CQA is a specialized application which deals with
information retrieval which has an ability to retrieve the right answers to
questions posed in natural language. Natural Language Processing (NLP)
techniques used to process a question, then searches for the required
information regarding user questions to determine the answer accurately. This
survey mainly focuses on different approaches to solve the problems arising due
to the lexical gap and also rank the accurate answers in question and answering
blogs such as community question answering websites.

 

Keywords: Community Question
Answering system, Information Retrieval, Lexical Gap, Natural
Language Processing.

 

1.     
INTRODUCTION

In recent
years, large amount of memory is employed by the historical web pages to
retrieve the vital information, from those pages mainly in the blogs such as
traditional Frequently Asked Questions (FAQ) archives and the emerging
Community Question Answering (CQA) services, such as Yahoo! Answers, Live QnA, HealthTap and Zhihu.com. The web
content of these web sites is usually organized as questions and the answers
associated with metadata from which the requesting users categorize the questions
and the respondents reply the best answers. This results in CQA archives to
have valuable resources for various tasks like question-answering and knowledge
mining, etc. One fundamental task for reusing the contents in CQA is to find
similar questions for newly queried questions, as questions are the keys to
accessing the knowledge in CQA. Then, the best answers to those similar
questions used to answer the queried questions, results in Question retrieval.

 

 

Fig
1.Question answering system

 

1.1.  
QUESTION ANSWERING TYPES

 

·        
Question classification:

Open-domain QAS is deals
with questions of nearly everything and Closed-domain QAS deals with questions
in a specific domain.

 

·        
Data Source classification:

Structured data deals with
relational database and Unstructured data deals with documents / web pages in
Internet

 

·        
Answer classification:

Extracted answer lies
directly in the database and Generated answer needs to be generated or
formulated from the retrieved data.

 

Fig 2. Architecture of question
answering system

 

·        
Question
Processing used to analyze sentence tagging, sentiment analysis and also
classify question to determine the intention of the question It also does
question reformulation so that the document processing can handle it.

 

·        
Document
Processing in Information Retrieval (IR) module used to retrieve the most
Relevant data from the system.

 

·        
Answer processing
used to identify useful information from the document and also rank the answers
if there are multiple answers and returns the most relevant answer 4.

 

1.2       QUESTION
CLASSIFICATION AND ANSWER

 

In Question processing the
system first should analyze the type of question. Questions can be classified
into two categories:

Question with ‘WH’ question
words such as what, where, who, whom, which, how, why and etc.

Questions with ‘modal’ or
‘auxiliary’ verbs that their answers are Yes/No4.Table 1 shows question
words, type of questions and answers.

 

 

 

 

 

 

 

 

 

 

 

 

Table 1. Question classification and
answer

 

 

1.3       APPROACHES
IN QUESTION ANSWERING SYSTEMS

 

·        
Linguistic Approach

 

Linguistic approach
understands natural language text, linguistic & common knowledge Linguistic
techniques such as tokenization, POS tagging and parsing. These were
implemented to user’s question for formulating it into a precise query that extracts
the respective response from the structured database.

 

·        
Statistical Approach

 

Availability of huge amount
of data on internet increased the importance of statistical approaches. A
statistical learning method gives the better results than other approaches.
Online text repositories and statistical approaches are independent of
structured query languages and can formulate queries in natural language form.
Mostly all Statistical Based QA system applied a statistical technique in QA system
such as Support vector machine classifier, Bayesian Classifiers, maximum
entropy models.

 

·        
Pattern Matching Approach

 

Pattern matching approach
deals with expressive power of text pattern, it replace the sophisticated
processing involved in other computing approaches. Most of the pattern matching
QA systems uses the surface text pattern, while some of them also rely on
templates for response generator.

 

·        
Deep Learning approach

 

Most of the Deep Learning
methods is used to implement one or more component of QAS such as question
classification, sentence selection, etc. It converts Natural Language into a
computable form e.g. using word embedding or using Neural Language Model e.g.
using RNN / LSTM or C.

 

 

2.      RESEARCH BACKGROUND

 

2.1                
COMMUNITY QUESTION ANSWERING

 

·        
Community Question answering
(CQA) is a computer science discipline within the fields of information
retrieval and natural language processing (NLP), which is concerned with
building systems that automatically answer questions posed by humans in a
natural language. A CQA implementation, usually a computer program, may
construct its answers by querying a structured database of knowledge or
information, usually a knowledge base. More commonly, CQA systems can pull
answers from an unstructured collection of natural language documents. Table 2
shows the comparison between the CQA and QA.

·        
In many cases, the community
generated content, however, may not be directly usable due to the vocabulary
gap. Users with diverse backgrounds do not necessarily to share the same
vocabulary. Stack Overflow, one of the technical question answering sites for
users can ask technical related questions. The entire technical questions are
solved by technical experts, where different users ask different questions for
the similar answer and there is a gap exists between what is asked and what is
answered either syntactically or semantically and such gaps ends in lexical
gap.

 

2.2       NATURAL LANGUAGE PROCESSING
(NLP)

 

·        
Natural language processing can be
defined as the ability of a machine to analyse, understand, and generate human
speech. The goal of NLP is to make interactions between computers and humans
feel exactly like interactions between humans and humans 1. CQA system take a
natural language question as input, convert the question into a query and
forwards it to the next module. When a set of required documents is retrieved,
the CQA system extracts an accurate answer for user posted question 5.

·        
Deep Analytics: Deep analytics involves
the application of advanced data processing techniques in order to extract
specific information from large or multi-source data sets. Deep analytics is
often used in the financial sector, the scientific community, the
pharmaceutical sector, and biomedical industries. Increasingly, however, deep
analysis is also being used by organizations and companies interested in mining
data of business value from expansive sets of consumer data.

·        
Machine Translation: Natural language
processing is increasingly being used for machine translation programs, in
which one human language is automatically translated into another human
language.

 

2.3       INFORMATION RETRIEVAL

 

An information
retrieval process begins when a user enters a query into the system. Queries
are formal statements of information needs, for example search strings in web
search engines.

In information
retrieval a query does not uniquely identify a single object in the collection.
Instead, several objects may match the query, perhaps with different degrees of
relevancy. User queries are matched against the database information. However,
as opposed to classical SQL queries of a database, in information retrieval the
results returned may or may not match the query, so results are typically
ranked. This ranking of results is a key difference of information retrieval
searching compared to database searching.

 

2.4       LEXICAL GAP

 

Lexical gaps
are instances of lack of lexicalization detected in a language while comparing
two languages or in a target language during translation. Although the problem
seems to be minor and clear, one gets rather the opposite impression after an
excursion through the linguistic literature on lexical gaps.
Different living environment and experience causes some lexical meanings in one
language does not exist in another language. 
Lexical gaps are filled by means of Hypernyms or by Antonymous
Expressions

 

2.5       RANK BASED ANSWER GENERATOR

 

Rank based
answer generation is to determine that the user post their exact questions and
obtain answers from other users. This helps to get better answers than compared
with search engines. The generated answers will be better than the answers
searched by the browsers and accurate result is determined. A enormous number
of QA pairs

 

have been
gathered in their repositories, and it also preserve the answers of the search
results. The Support Vector Machine algorithm is a retrieval
function that uses pairwise ranking
methods to sort the accurate results based on how relevant they are to a particular
user query.

3.     
RELATED WORK ON COMMUNITY QUESTION
ANSWERING

 

3.1       Al-Harbi O., Jusoh S., and  Norwawi N. (2011) 1

 

Resolving the
lexical ambiguity problem by combining two pieces of knowledge; context
knowledge and ontology of concepts knowledge of interesting domain, into
shallow natural language processing (SNLP). The combination of these knowledge
is used to decide the most possible meaning of the word. It lacks in resolving
the syntactic ambiguity in natural language questions.

 

3.2       Bernhard D., and Gurevych I. (2009) 2

 

Monolingual translation
probabilities have recently been introduced in retrieval models to solve the
lexical gap problem. It is evaluated
with three datasets for training statistical word translation models for use in
answer finding question-answer pairs, manually-tagged question reformulations
and glosses for the same term extracted from several lexical semantic
resources.The existing system lacks in question analysis by automatically
identifying question topic and question focus.

 

3.3       Guangyou Zhou, Zhiwen Xie, Tingting He.
(2016) 3

 

The
State-of-the-art approaches address these issues by implicitly expanding the
queried questions with additional words or phrases using monolingual
translation models. The task of question retrieval in CQA and represent a
question as Bag-of-Embedded-Words (BoEW) in a continuous space. The existing
system lacks in pairs to learn various translation models to bridge the lexical
gap problem.

 

3.4       Qiu X., Huang X. (2015)6

 

The
convolutional neural tensor network architecture to encode the sentences in
semantic space and model their interactions with a tensor layer and also help
to learn better word embeddings. The existing system lacks in to efficiently
detect local reuses at the semantic level for large scale problems.

 

3.5       Shen Y., Rong W., Jiang N., Peng B., Tang
J., and Xiong Z. (2017) 7

 

A Word
Embedding based Correlation (WEC) model is proposed by integrating advantages
of both the translation model and word embedding and also leverages the
continuity and smoothness of continuous space word representation to deal with
new pairs of words that are rare in the training parallel text. It is necessary
to focus on question-question matching tasks, or Multilanguage question
retrieval task. It does not solve parallel detection problems.

 

3.6       Wei-Nan Zhang, Zhao Yan Ming, Yu Zhang.
(2016) 8

 

It Explore the
key concept identification approach for query refinement and a pivot language
translation based approach to explore key concept paraphrasing. These word
embedding models contribute the most the performance. The existing system
generates noise samples for each input word to estimate the target word causes
inefficiency.

 

3.7       Zhang K., Wu W., Wu H., Li Z., Zhou M.
(2014) 9

 

They are
heterogeneous for both the literal level and user behaviours. Conduct a series
of experiments to evaluate our proposed approaches automatically on large-scale
data sets. The existing system cannot be directly used for large scale
problems.

 

 

 

3.8       Zhou G., Chen Y., Zeng D., and Zhao J.
(2013) 11

 

A novel
Question-Answer Topic Model (QATM) to learn the latent topics aligned across
the question-answer pairs to alleviate the lexical gap problem. A faster and
better retrieval model for question search by leveraging user chosen category.
The existing system lacks in the localness and hierarchy intrinsic to the
natural language problems.

 

3.9       Zhou G., He T., Zhao J., Hu P. (2015)
12

 

The framework
of fisher kernel to aggregated them into the fixed length vectors. That
metadata of category information benefits the word embedding learning for
question representation. The existing system have problem from different
aspects such as extraction methods with or without linguistic knowledge.

 

3.10     Zhou G., Cai L., Zhao J., and Liu K. (2011)
10

 

A machine
learning algorithm that aims to predict a ranking among all the possible
labels, to perform question classification. Training process does not need many
training data, which are always expensive to obtain in the CQA services.
Combining the bilingual translation or category information can be done for
better question retrieval. Sometimes when we search a query in the services,
the system always tells us it cannot find any results.

 

4.     
COMPARISON TABLE

 

Table 2.
Comparison of CQA and QA

 

 

5.      PROPOSED
WORK

 

We present a
novel approach to determine accurate answer in CQA based on user questions
instead of retrieving the entire document. It categorized into offline learning
and online search component. In the offline learning component, instead of
prolonged process we construct the positive, neutral, and negative training
samples in the forms of preference pairs.We then propose a robust pairwise
learning to rank model to incorporate these three types of training samples. In
the online search component, for a given question, we first collect a pool of
answer candidates via finding its similar questions. We then employ the offline
learned model to rank the answer candidates via pair wise comparison.

6.      Conclusion and Future Work

·        
The main goal of a community
question answering system is to retrieving answers to questions rather than
full documents as most information retrieval systems. Therefore we analyzed the different approaches to solve
the problem arising due to lexical gap and also determine to rank the accurate answers
for the user question instead of retrieving the entire document.Lexical Gap is complex phenomenon, in which it requires many factors
to obtain exact answers for the causes of classification and translation
strategies.

 

·        
The future work towards multilingual,
multi-knowledge source CQA systems that are capable of understanding noisy,
human natural language input and also to overcome the problems in pairwise
ranking such as noise-sensitive, large-scale preference pairs, and loss of
information about the finer granularity in the field of CQA.

 

 

REFERENCES

 

1                                       
Al-Harbi O.,
Jusoh S., and  Norwawi N. (2011),’Lexical Disambiguation in Natural Language
Questions (NLQs)’, in IJCSI International Journal of Computer Science
Issues, Vol. 8, Issue 4, No 2.

2                                       
Bernhard D., and
Gurevych I. (2009), ‘Combining lexical
semantic resources with question & answer archives for translation-based
answer finding’, in Proceedings of the ACL, pp. 728–736.

3                                       
Guangyou Zhou,
Zhiwen Xie, Tingting He. (2016),’Question-answer
topic model for question retrieval in community question answering’, in
IEEE/ACM Transactions on Audio, Speech, and Language Processing ( Volume: 24,
Issue:7).

4                                       
Pawan Kumar., Raj
Kumar Goel., and Prem Sagar Sharma. (2014), ‘A New Architecture of Automatic Question Answering System using
Ontology’ in International Journal of Computer Applications (0975 – 8887)
Volume 97 – No.20.

5                                       
Poonam Gupta.,
and Vishal Gupta. (2012), ‘A Survey of
Text Question Answering Techniques’ in International Journal of Computer
Applications (0975 – 8887) Volume 53– No.4.

6                                       
Qiu X., Huang X
.(2015), ‘Convolutional neural tensor
network architecture for community-based question answering’, in IJCAI
proceedings of the twenty fourth international joint conference on artificial
intelligence.

7                                       
Shen Y., Rong W.,
Jiang N., Peng B., Tang J., and Xiong Z. (2017), ‘Word Embedding Based Correlation Model for Question/Answer Matching’, in
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.

8                                       
Wei-Nan Zhang,
Zhao Yan Ming, Yu Zhang .(2016), ‘Capturing
the Semantics Of Key Phrases Using Multiple Languages For Question Retrieval’,
in IEEE Transactions on Knowledge and Data Engineering ( Volume: 28, Issue: 4).

9                                       
Zhang K., Wu W.,
Wu H., Li Z., Zhou M. (2014), ‘Question
Retrieval With High Quality Answers In Community Question Answering’, Proceedings
of the 23rd ACM International Conference on Conference on Information and
Knowledge Management pp. 371-380.

10                                   
Zhou G., Cai L.,
Zhao J., and Liu K. (2011), ‘Phrase-based
translation model for question retrieval in community question answer
archives’, in Proceedings of the ACL, pp. 653–662.

11                                   
Zhou G., Chen Y.,
Zeng D., and Zhao J. (2013), ‘Towards
Faster And Better Retrieval Models For Question Search’, CIKM Proceedings
of the 22nd ACM international conference on Information & Knowledge
Management, pp. 2139- 2148.

12                                   
Zhou G., He T.,
Zhao J., Hu P. G.(2015), ‘Learning
Continuous Word Embedding With Metadata For Question Retrieval In Community
Question Answering’, Proceedings of the 53rd Annual Meeting of the Association
for Computational Linguistics and the 7th International Joint Conference on
Natural Language Processing, pages 250–259.