10 research outputs found
Ontology-Based Sentence Extraction for Answering Why-Question
Most studies on why-question answering system usually used the keyword-based approaches. They rarely involved domain ontology in capturing the semantic of the document contents, especially in detecting the presence of the causal relations. Consequently, the word mismatch problem usually occurs and the system often retrieves not relevant answers. For solving this problem, we propose an answer extraction method by involving the semantic similarity measure, with selective causality detection. The selective causality detection is applied because not all sentences belonging to an answer contain causality. Moreover, the motivation of the use of semantic similarity measure in scoring function is to get more moderate results about the presence of the semantic annotations in a sentence, instead of 0/1. The semantic similarity measure employed is based on the shortest path and the maximum depth of the ontology graph. The evaluation is conducted by comparing the proposed method against the comparable ontology-based methods, i.e., the sentence extraction with Monge-Elkan with 0/1 internal similarity function. The proposed method shows the improvements in term of MRR (16%, 0.79-0.68), P@1 (15%, 0.76-0.66), P@5 (14%, 0.8-0.7), and Recall (19%, 0.86-0.72)
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
Learning to represent, categorise and rank in community question answering
The task of Question Answering (QA) is arguably one of the oldest tasks in Natural Language Processing, attracting high levels of interest from both industry and academia. However, most research has focused on factoid questions, e.g. Who is the president of Ireland? In contrast, research on answering non-factoid questions, such as manner, reason, difference and opinion questions, has been rather piecemeal.
This was largely due to the absence of available labelled data for the task. This is changing, however, with the growing popularity of Community Question Answering (CQA) websites, such as Quora, Yahoo! Answers and the Stack Exchange family of forums. These websites provide natural labelled data allowing us to apply machine learning techniques.
Most previous state-of-the-art approaches to the tasks of CQA-based question answering involved handcrafted features in combination with linear models. In this thesis we hypothesise that the use of handcrafted features can be avoided and the tasks can be approached with representation learning techniques, specifically deep learning.
In the first part of this thesis we give an overview of deep learning in natural language processing and empirically evaluate our hypothesis on the task of detecting semantically equivalent questions, i.e. predicting if two questions can be answered by the same answer.
In the second part of the thesis we address the task of answer ranking, i.e. determining how suitable an answer is for a given question. In order to determine the suitability of representation learning for the task of answer ranking, we provide a rigorous experimental evaluation of various neural architectures, based on feedforward, recurrent and convolutional neural networks, as well as their combinations.
This thesis shows that deep learning is a very suitable approach to CQA-based QA, achieving state-of-the-art results on the two tasks we addressed
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
Learning to Rank for Why-Question Answering
Contains fulltext :
86344.pdf (publisher's version ) (Open Access)26 p
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition. Volume 2 : Traitement Automatique des Langues Naturelles
@ 6ème conférence conjointe: JEP-TALN-RECITAL 2020no abstrac