14,225 research outputs found
Answer Extraction with Multiple Extraction Engines for Web-Based Question Answering
Abstract. Answer Extraction of Web-based Question Answering aims to extract answers from snippets retrieved by search engines. Search results contain lots of noisy and incomplete texts, thus the task becomes more challenging comparing with traditional answer extraction upon offline corpus. In this paper we discuss the important role of employing multiple extraction engines for Web-based Question Answering. Aggregating multiple engines could ease the negative effect from the noisy search results on single method. We adopt a Pruned Rank Aggregation method which performs pruning while aggregating candidate lists provided by multiple engines. It fully leverages redundancies within and across each list for reducing noises in candidate list without hurting answer recall. In addition, we rank the aggregated list with a Learning to Rank framework with similarity, redundancy, quality and search features. Experiment results on TREC data show that our method is effective for reducing noises in candidate list, and greatly helps to improve answer ranking results. Our method outperforms state-of-the-art answer extraction method, and is sufficient in dealing with the noisy search snippets for Web-based QA
An embodied conversational agent for intelligent web interaction on pandemic crisis communication
In times of crisis, an effective communication mechanism is paramount in providing accurate and timely information to the community. In this paper we study the use of an intelligent embodied conversational agent (EGA) as the front end interface with the public for a Crisis Communication Network Portal (CCNet). The proposed system, CCNet, is an integration of the intelligent conversation agent, AINI, and an Automated Knowledge Extraction Agent (AKEA). AKEA retrieves first hand information from relevant sources such as government departments and news channels. In this paper, we compare the interaction of AINI against two popular search engines, two question answering systems and two conversational systems
Mobile Phone Text Processing and Question-Answering
Mobile phone text messaging between mobile users and information services is a growing area of
Information Systems. Users may require the service to provide an answer to queries, or may, in wikistyle, want to contribute to the service by texting in some information within the service’s domain of discourse. Given the volume of such messaging it is essential to do the processing through an automated service. Further, in the case of repeated use of the service, the quality of such a response has the potential to benefit from a dynamic user profile that the service can build up from previous texts of the same user.
This project will investigate the potential for creating such intelligent mobile phone services and aims to produce a computational model to enable their efficient implementation. To make the project feasible, the scope of the automated service is considered to lie within a limited domain of, for example, information about entertainment within a specific town centre. The project will assume the existence of a model of objects within the domain of discourse, hence allowing the analysis of texts within the context of a user model and a domain model. Hence, the project will involve the subject areas of natural language processing, language engineering, machine learning, knowledge extraction, and ontological engineering
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model
Semantic parsing shines at analyzing complex natural language that involves
composition and computation over multiple pieces of evidence. However, datasets
for semantic parsing contain many factoid questions that can be answered from a
single web document. In this paper, we propose to evaluate semantic
parsing-based question answering models by comparing them to a question
answering baseline that queries the web and extracts the answer only from web
snippets, without access to the target knowledge-base. We investigate this
approach on COMPLEXQUESTIONS, a dataset designed to focus on compositional
language, and find that our model obtains reasonable performance (35 F1
compared to 41 F1 of state-of-the-art). We find in our analysis that our model
performs well on complex questions involving conjunctions, but struggles on
questions that involve relation composition and superlatives.Comment: *sem 201
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