263 research outputs found
Large-Scale Goodness Polarity Lexicons for Community Question Answering
We transfer a key idea from the field of sentiment analysis to a new domain:
community question answering (cQA). The cQA task we are interested in is the
following: given a question and a thread of comments, we want to re-rank the
comments so that the ones that are good answers to the question would be ranked
higher than the bad ones. We notice that good vs. bad comments use specific
vocabulary and that one can often predict the goodness/badness of a comment
even ignoring the question, based on the comment contents only. This leads us
to the idea to build a good/bad polarity lexicon as an analogy to the
positive/negative sentiment polarity lexicons, commonly used in sentiment
analysis. In particular, we use pointwise mutual information in order to build
large-scale goodness polarity lexicons in a semi-supervised manner starting
with a small number of initial seeds. The evaluation results show an
improvement of 0.7 MAP points absolute over a very strong baseline and
state-of-the art performance on SemEval-2016 Task 3.Comment: SIGIR '17, August 07-11, 2017, Shinjuku, Tokyo, Japan; Community
Question Answering; Goodness polarity lexicons; Sentiment Analysi
Word Embedding based Correlation Model for Question/Answer Matching
With the development of community based question answering (Q&A) services, a
large scale of Q&A archives have been accumulated and are an important
information and knowledge resource on the web. Question and answer matching has
been attached much importance to for its ability to reuse knowledge stored in
these systems: it can be useful in enhancing user experience with recurrent
questions. In this paper, we try to improve the matching accuracy by overcoming
the lexical gap between question and answer pairs. A Word Embedding based
Correlation (WEC) model is proposed by integrating advantages of both the
translation model and word embedding, given a random pair of words, WEC can
score their co-occurrence probability in Q&A pairs and it can also leverage the
continuity and smoothness of continuous space word representation to deal with
new pairs of words that are rare in the training parallel text. An experimental
study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new
method's promising potential.Comment: 8 pages, 2 figure
Understanding and exploiting user intent in community question answering
A number of Community Question Answering (CQA) services have emerged
and proliferated in the last decade. Typical examples include Yahoo! Answers,
WikiAnswers, and also domain-specific forums like StackOverflow. These services
help users obtain information from a community - a user can post his or her questions which may then be answered by other users. Such a paradigm of information seeking is particularly appealing when the question cannot be answered directly by Web search engines due to the unavailability of relevant online content. However, question submitted to a CQA service are often colloquial and ambiguous. An accurate understanding of the intent behind a question is important for satisfying the user's information need more effectively and efficiently.
In this thesis, we analyse the intent of each question in CQA by classifying
it into five dimensions, namely: subjectivity, locality, navigationality, procedurality,
and causality. By making use of advanced machine learning techniques, such
as Co-Training and PU-Learning, we are able to attain consistent and significant
classification improvements over the state-of-the-art in this area. In addition to
the textual features, a variety of metadata features (such as the category where
the question was posted to) are used to model a user's intent, which in turn help
the CQA service to perform better in finding similar questions, identifying relevant
answers, and recommending the most relevant answerers.
We validate the usefulness of user intent in two different CQA tasks. Our
first application is question retrieval, where we present a hybrid approach which
blends several language modelling techniques, namely, the classic (query-likelihood)
language model, the state-of-the-art translation-based language model, and our
proposed intent-based language model. Our second application is answer validation, where we present a two-stage model which first ranks similar questions by using
our proposed hybrid approach, and then validates whether the answer of the top
candidate can be served as an answer to a new question by leveraging sentiment
analysis, query quality assessment, and search lists validation
Review Paper on Answers Selection and Recommendation in Community Question Answers System
Nowadays, question answering system is more convenient for the users, users ask question online and then they will get the answer of that question, but as browsing is primary need for each an individual, the number of users ask question and system will provide answer but the computation time increased as well as waiting time increased and same type of questions are asked by different users, system need to give same answers repeatedly to different users. To avoid this we propose PLANE technique which may quantitatively rank answer candidates from the relevant question pool. If users ask any question, then system provide answers in ranking form, then system recommend highest rank answer to the user. We proposing expert recommendation system, an expert will provide answer of the question which is asked by the user and we also implement sentence level clustering technique in which a single question have multiple answers, system provide most suitable answer to the question which is asked by the user
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