共查询到18条相似文献，搜索用时 406 毫秒
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随着网络的快速发展,人们对搜索引擎的依赖越来越强。传统搜索引擎仅基于关键字,而问答系统能够快速、准确地获取用户所需信息,是新一代搜索引擎。问答系统允许用户使用自然语言提问,能够准确返回用户所需答案。问答系统一般由问题理解、信息检索、答案抽取三部分组成。本文介绍了问题理解中的问题类型分类技术,并给出了具体实现。 相似文献
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自动问答系统在搜索引擎的基础上融入了自然语言的知识与应用,与传统的依靠关键字匹配的搜索引擎相比,能够更好地满足用户的检索需求。介绍了计算机操作系统自动问答系统模型,阐述了具体开发过程,设计并实现了基于计算机操作系统领域的自动问答系统,实践表明该系统能够较为准确地回答用户问题。 相似文献
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互连网上信息浩瀚无限,各种搜索引擎是人们获得信息常用的工具,但是它的可用性和易用性还有待进步提高。为了更有效的获得用自然语言提问的问题的答案,本文提出了运用中文分词技术在获取的搜索网页的基础上进行全文检索和进行问题答案匹配,获得对应的答案列表的一种实现方法 相似文献
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中外主流搜索引擎搜索能力研究 总被引：2，自引：0，他引：2
通过对百度、中国搜索和Google等中外主流搜索引擎搜索能力的对比分析,找出国内主流搜索搜索引擎的优势和存在的不足,为国内搜索引擎的发展和用户的使用提供一些借鉴。 相似文献
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全文搜索引擎是目前比较有效的网络信息搜索工具。本文比较了谷歌、百度、必应、雅虎、搜狗、有道和搜搜等常用的7种搜索引擎的学术信息搜索情况、特色功能和搜索语法。在介绍功能特色的基础上,分析了这些功能在学术信息检索中的应用特点。 相似文献
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交互问答平台是一种开放的服务平台,基于交互问答平台所建立的问答知识库可纳入到搜索信息源之中,提高搜索引擎的搜索质量.因此,国内外搜索引擎都致力于开发交互问答平台.本文比较了国内外6个有代表性的交互问答平台,分析各自的特色与不足之处,并从服务内容、服务手段等方面提出相应的发展对策. 相似文献
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【目的/意义】解决自动问答系统构建过程中数据集构建成本高的问题,以及自动问答过程中仅考虑问题或
答案本身相关性的局限。【方法/过程】提出了一种融合标注问答库和社区问答数据的数据集构建方法,构建问题关
键词-问题-答案-答案簇多层异构网络模型,并给出了基于该模型的自动问答算法。获取图书馆语料进行处理作
为实验数据,将BERT-Cos、AINN、BiMPM模型作为对比对象进行了实验与分析。【结果/结论】通过实验得到了各
模型在图书馆自动问答任务上的效果,本文所提模型在各评价指标上均优于其他模型,模型准确率达87.85%。【创
新/局限】本文提出的多数据源融合数据集构建方法和自动问答模型在问答任务中相对于已有方法具有更好的表
现,同时根据模型效果分析给出用户提问词长建议。 相似文献
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【目的/意义】我国“互联网+政务服务”发展迅速,在线政务服务资源日益丰富,民众能否通过搜索引擎查
找到政务服务是影响在线政务服务平台成效的重要因素。政务服务资源搜索的主要目标是“查准”,研究提出关于
中美政务服务资源搜索引擎可见性的八个假设。【方法/过程】按照查找典型政务服务个人事项“申领机动车驾驶
证”和法人事项“注册有限责任公司”的需求构造中英文关键词,分别通过百度和谷歌,以定位到中国各省和美国各
州经济最发达城市为目标进行搜索实验,采集首页搜索结果并进行相关性评分。在此基础上进行搜索引擎搜索功
能的统计分析,用非参数检验验证假设。【结果/结论】研究认为搜索引擎理解政务服务词汇的能力直接影响了其搜
索水平,政务服务平台也可以通过搜索引擎优化提升可见性。【创新/局限】研究创新在于构建中英文关键词,直接
采集百度和谷歌的搜索结果进行跨国比较研究,突破了以往同类研究的宽度和深度;局限主要在于相关性判断存
在主观性和搜索对象的规模较小。 相似文献
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With the advances in natural language processing (NLP) techniques and the need to deliver more fine-grained information or answers than a set of documents, various QA techniques have been developed corresponding to different question and answer types. A comprehensive QA system must be able to incorporate individual QA techniques as they are developed and integrate their functionality to maximize the system’s overall capability in handling increasingly diverse types of questions. To this end, a new QA method was developed to learn strategies for determining module invocation sequences and boosting answer weights for different types of questions. In this article, we examine the roles and effects of the answer verification and weight boosting method, which is the main core of the automatically generated strategy-driven QA framework, in comparison with a strategy-less, straightforward answer-merging approach and a strategy-driven but with manually constructed strategies. 相似文献
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This paper describes how questions can be characterized for question answering (QA) along different facets and focuses on questions that cannot be answered directly but can be divided into simpler ones so that they can be answered directly using existing QA capabilities. Since individual answers are composed to generate the final answer, we call this process as compositional QA. The goal of the proposed QA method is to answer a composite question by dividing it into atomic ones, instead of developing an entirely new method tailored for the new question type. A question is analyzed automatically to determine its class, and its sub-questions are sent to the relevant QA modules. Answers returned from the individual QA modules are composed based on the predetermined plan corresponding to the question type. The experimental results based on 615 questions show that the compositional QA approach outperforms the simple routing method by about 17%. Considering 115 composite questions only, the F-score was almost tripled from the baseline. 相似文献
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Question answering (QA) aims at finding exact answers to a user’s question from a large collection of documents. Most QA systems combine information retrieval with extraction techniques to identify a set of likely candidates and then utilize some ranking strategy to generate the final answers. This ranking process can be challenging, as it entails identifying the relevant answers amongst many irrelevant ones. This is more challenging in multi-strategy QA, in which multiple answering agents are used to extract answer candidates. As answer candidates come from different agents with different score distributions, how to merge answer candidates plays an important role in answer ranking. In this paper, we propose a unified probabilistic framework which combines multiple evidence to address challenges in answer ranking and answer merging. The hypotheses of the paper are that: (1) the framework effectively combines multiple evidence for identifying answer relevance and their correlation in answer ranking, (2) the framework supports answer merging on answer candidates returned by multiple extraction techniques, (3) the framework can support list questions as well as factoid questions, (4) the framework can be easily applied to a different QA system, and (5) the framework significantly improves performance of a QA system. An extensive set of experiments was done to support our hypotheses and demonstrate the effectiveness of the framework. All of the work substantially extends the preliminary research in Ko et al. (2007a). A probabilistic framework for answer selection in question answering. In: Proceedings of NAACL/HLT. 相似文献
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This paper reports results from a study exploring the multimedia search functionality of Chinese language search engines. Web searching in Chinese (Mandarin) is a growing research area and a technical challenge for popular commercial Web search engines. Few studies have been conducted on Chinese language search engines. We investigate two research questions: which Chinese language search engines provide multimedia searching, and what multimedia search functionalities are available in Chinese language Web search engines. Specifically, we examine each Web search engine’s (1) features permitting Chinese language multimedia searches, (2) extent of search personalization and user control of multimedia search variables, and (3) the relationships between Web search engines and their features in the Chinese context. Key findings show that Chinese language Web search engines offer limited multimedia search functionality, and general search engines provide a wider range of features than specialized multimedia search engines. Study results have implications for Chinese Web users, Website designers and Web search engine developers. 相似文献
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Alberto Téllez-Valero Manuel Montes-y-Gómez Luis Villaseñor-Pineda Anselmo Peñas Padilla 《Information processing & management》2011
Question answering (QA) is the task of automatically answering a question posed in natural language. Currently, there exists several QA approaches, and, according to recent evaluation results, most of them are complementary. That is, different systems are relevant for different kinds of questions. Somehow, this fact indicates that a pertinent combination of various systems should allow to improve the individual results. This paper focuses on this problem, namely, the selection of the correct answer from a given set of responses corresponding to different QA systems. In particular, it proposes a supervised multi-stream approach that decides about the correctness of answers based on a set of features that describe: (i) the compatibility between question and answer types, (ii) the redundancy of answers across streams, as well as (iii) the overlap and non-overlap information between the question–answer pair and the support text. Experimental results are encouraging; evaluated over a set of 190 questions in Spanish and using answers from 17 different QA systems, our multi-stream QA approach could reach an estimated QA performance of 0.74, significantly outperforming the estimated performance from the best individual system (0.53) as well as the result from best traditional multi-stream QA approach (0.60). 相似文献
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《Information processing & management》2023,60(3):103296
Recent studies point out that VQA models tend to rely on the language prior in the training data to answer the questions, which prevents the VQA model from generalization on the out-of-distribution test data. To address this problem, approaches are designed to reduce the language distribution prior effect by constructing negative image–question pairs, while they cannot provide the proper visual reason for answering the question. In this paper, we present a new debiasing framework for VQA by Learning to Sample paired image–question and Prompt for given question (LSP). Specifically, we construct the negative image–question pairs with certain sampling rate to prevent the model from overly relying on the visual shortcut content. Notably, question types provide a strong hint for answering the questions. We utilize question type to constrain the sampling process for negative question–image pairs, and further learn the question type-guided prompt for better question comprehension. Extensive experiments on two public benchmarks, VQA-CP v2 and VQA v2, demonstrate that our model achieves new state-of-the-art results in overall accuracy, i.e., 61.95% and 65.26%. 相似文献
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《Information processing & management》2023,60(3):103297
Humans are able to reason from multiple sources to arrive at the correct answer. In the context of Multiple Choice Question Answering (MCQA), knowledge graphs can provide subgraphs based on different combinations of questions and answers, mimicking the way humans find answers. However, current research mainly focuses on independent reasoning on a single graph for each question–answer pair, lacking the ability for joint reasoning among all answer candidates. In this paper, we propose a novel method KMSQA, which leverages multiple subgraphs from the large knowledge graph ConceptNet to model the comprehensive reasoning process. We further encode the knowledge graphs with shared Graph Neural Networks (GNNs) and perform joint reasoning across multiple subgraphs. We evaluate our model on two common datasets: CommonsenseQA (CSQA) and OpenBookQA (OBQA). Our method achieves an exact match score of 74.53% on CSQA and 71.80% on OBQA, outperforming all eight baselines. 相似文献
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《Information processing & management》2016,52(2):340-357
Question Answering (QA) systems are developed to answer human questions. In this paper, we have proposed a framework for answering definitional and factoid questions, enriched by machine learning and evolutionary methods and integrated in a web-based QA system. Our main purpose is to build new features by combining state-of-the-art features with arithmetic operators. To accomplish this goal, we have presented a Genetic Programming (GP)-based approach. The exact GP duty is to find the most promising formulas, made by a set of features and operators, which can accurately rank paragraphs, sentences, and words. We have also developed a QA system in order to test the new features. The input of our system is texts of documents retrieved by a search engine. To answer definitional questions, our system performs paragraph ranking and returns the most related paragraph. Moreover, in order to answer factoid questions, the system evaluates sentences of the filtered paragraphs ranked by the previous module of our framework. After this phase, the system extracts one or more words from the ranked sentences based on a set of hand-made patterns and ranks them to find the final answer. We have used Text Retrieval Conference (TREC) QA track questions, web data, and AQUAINT and AQUAINT-2 datasets for training and testing our system. Results show that the learned features can perform a better ranking in comparison with other evaluation formulas. 相似文献