Comprehensive Survey on Community Question
Answering System to solve the Lexical Gap
Computer Science and Engineering
Computer Science and Engineering
Computer Science and Engineering
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 cause information retrieval a time consuming 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. The proposed
approach is to solve the problems arising due to the lexical gap in question
and answering blogs such as community question answering websites.
Keywords—Community Question Answering system, Information Retrieval, Natural Language Processing.
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, and Baidu Zhidao. 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.
Question answering system
QUESTION ANSWERING TYPES
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
Extracted answer lies
directly in the database and Generated answer needs to be generated or
formulated from the retrieved data.
Architecture of question
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
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
Question Classification and Answer
In Question processing the
system first should analyze the type of question. Table 1 shows question words,
type of questions and answers. 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/No.
1 Question classification and answer
Approaches in Question Answering Systems
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.
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
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.
Community Question Answering
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
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 Comparison of CQA and QA
Natural Language Processing (NLP)
language processing can be defined as the ability of a machine to analyze,
understand, and generate human speech. The goal of NLP is to make interactions
between computers and humans feel exactly like interactions between humans and
Sentence Segmentation, Part-of-speech Tagging, and
Parsing: Natural language processing can be used to
analyze parts of a sentence to better understand the grammatical construction
of the sentence.
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.
Natural language processing is increasingly being used for machine translation
programs, in which one human language is automatically translated into another
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
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.
work on community question answering
Bernhard D., and Gurevych I. (2009)1: 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.
Guangyou Zhou, Zhiwen Xie, Tingting He. (2016)4: 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.
Wei-Nan Zhang, Zhao Yan Ming, Yu Zhang. (2016)10: 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.
Qiu X., Huang X. (2015)7: 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.
Zhou G., He T., Zhao J., Hu P. (2015)15: 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
Zhang K., Wu W., Wu H., Li Z., Zhou M. (2014)12: 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
Zhou G., Chen Y., Zeng D., and Zhao J. (2013)14: 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.
Zhou G., Liu.Y., Liu F., Zeng D., and Zhao J. (2011)15: 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.
Shen Y., Rong W., Jiang N., Peng B., Tang J., and Xiong
Z. (2017) 14: 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
Al-Harbi O., Jusoh S., and Norwawi N. (2011) 15: 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.
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.
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).
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).
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.
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.
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.
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.
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,
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.
Jusoh S., and Norwawi N. (2011), ‘Lexical
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