共查询到20条相似文献，搜索用时 46 毫秒
1.
【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
2.
【目的/意义】为揭示社会化问答情境下用户知识内化过程和内在动因,提升社会化问答社区知识的利用率
和重用率。【方法/过程】本研究基于同化顺应理论、信息加工学习理论构建了社会化问答情境下用户知识内化过程
模型,分析其作用过程和机理。然后,从组态视角运用模糊集定性比较分析（fsQCA）方法分析了用户知识内化的动
因和影响路径。【结果/结论】研究发现：社会化问答情境下用户的知识内化是以用户已有的认知结构为制约机制,
知识经过同化和顺应后被元认知监控,然后由知识反馈调节,同时受平台传播能力和用户自身吸收能力两个主要
因素影响。促成社会化问答情境下用户知识内化发生的条件组态路径有用户促进型和平台促进型两类;抑制社会
化问答情境下用户知识内化发生的条件组态路径有用户抑制型和平台抑制型两类。【创新/局限】不同类型和场景
下社会化问答社区用户的知识内化差异化动因还需进一步研究。 相似文献
3.
【目的/意义】为在线医疗问诊平台中的医生自动生成高质量标签,更好地服务于对医生资源的分类、检索和管理。【方法/过程】基于在线问诊文本信息,提出了结合时间周期特征与文本主题特征的医生标签自动生成算法。首先根据医生相关文本信息提取关键词生成候选标签,然后从患者问题文本和医生回答文本两个方面进行LDA主题模型训练,按时间周期挖掘出问题文本和回答文本的主题特征,对候选标签进行质量控制;最后经标签加权混合后得到最终的医生标签。【结果/结论】实验结果表明,该标签自动生成算法能够反映出医生标签生成的动态性,能够准确生成符合医生专业知识特征的高质量标签,具有较好的标签生成效果。 相似文献
4.
This paper presents a roadmap of current promising research tracks in question answering with a focus on knowledge acquisition and reasoning. We show that many current techniques developed in the frame of text mining and natural language processing are ready to be integrated in question answering search systems. Their integration opens new avenues of research for factual answer finding and for advanced question answering. Advanced question answering refers to a situation where an understanding of the meaning of the question and the information source together with techniques for answer fusion and generation are needed. 相似文献
5.
Question answering websites are becoming an ever more popular knowledge sharing platform. On such websites, people may ask any type of question and then wait for someone else to answer the question. However, in this manner, askers may not obtain correct answers from appropriate experts. Recently, various approaches have been proposed to automatically find experts in question answering websites. In this paper, we propose a novel hybrid approach to effectively find experts for the category of the target question in question answering websites. Our approach considers user subject relevance, user reputation and authority of a category in finding experts. A user’s subject relevance denotes the relevance of a user’s domain knowledge to the target question. A user’s reputation is derived from the user’s historical question-answering records, while user authority is derived from link analysis. Moreover, our proposed approach has been extended to develop a question dependent approach that considers the relevance of historical questions to the target question in deriving user domain knowledge, reputation and authority. We used a dataset obtained from Yahoo! Answer Taiwan to evaluate our approach. Our experiment results show that our proposed methods outperform other conventional methods. 相似文献
6.
创业韧性对再创意愿的影响：双元学习与反事实思维的作用 《科学学研究》2022,40(3):505-515
基于经验学习与归因理论,构建有调节的双中介作用理论模型框架,研究失败情境下创业韧性与再创意愿的因果关系机制,重点探究双元学习在两者间的中介作用机理,同时引入反事实思维作为创业韧性对再创意愿作用机理的边界条件。实证研究发现：（1）创业韧性对再创意愿有积极促进作用;（2）双元学习在创业韧性与再创意愿之间有部分中介作用;（3）自我导向反事实思维调节创业韧性对探索式学习的直接作用以及其通过探索式学习对再创意愿的间接作用;（4）他人导向反事实思维调节创业韧性对利用式学习的直接作用以及其通过利用式学习对再创意愿的间接作用。因此,创业失败的创业者通过对失败事件的反思、解构与归因,进行有目的地进行创业学习,可以激发创业者的再创意愿,从而为创业失败者东山再起提供自我进取与社会支持的理论依据与决策借鉴。 相似文献
7.
Saeedeh Momtazi 《Information processing & management》2018,54(3):380-393
Question answering systems assist users in satisfying their information needs more precisely by providing focused responses to their questions. Among the various systems developed for such a purpose, community-based question answering has recently received researchers’ attention due to the large amount of user-generated questions and answers in social question-and-answer platforms. Reusing such data sources requires an accurate information retrieval component enhanced by a question classifier. The question classification gives the system the possibility to have information about question categories to focus on questions and answers from relevant categories to the input question. In this paper, we propose a new method based on unsupervised Latent Dirichlet Allocation for classifying questions in community-based question answering. Our method first uses unsupervised topic modeling to extract topics from a large amount of unlabeled data. The learned topics are then used in the training phase to find their association with the available category labels in the training data. The category mixture of topics is finally used to predict the label of unseen data. 相似文献
8.
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
9.
《Information processing & management》2022,59(4):102988
External stakeholders require accurate and explainable financial distress prediction (FDP) models. Complex machine learning algorithms offer high accuracy, but most of them lack explanatory power, resulting in external stakeholders being cautious in adopting them. Therefore, an explainable artificial intelligence approach including a whole process ensemble method and an explainable frame for FDP is here proposed. The ensemble algorithm from feature selection to predictor construction can achieve high accuracy according to the actual case, and the interpretation framework can meet the needs of external users by generating local explanations and global explanations. First, a two-stage scheme integrated with a filter and wrapper technique is designed for feature selection. Second, multiple ensemble models are explored and they are evaluated according to the actual case. Finally, Shapley additive explanations, counterfactual explanations and partial dependence plots are employed to enhance model interpretability. Taking financial data of Chinese listed companies from 2007 to 2020 as a dataset, the highest AUC is ensured by LightGBM with a value of 0.92. Local explanations help individual enterprises identify the key features which lead to their financial distress, and counterfactual explanations are produced to provide improvement strategies. By analyzing the features importance and the impact of feature interaction on the results, global explanations can improve the transparency and credibility of ‘black box’ models. 相似文献
10.
《Information processing & management》2020,57(6):102352
Intracerebral hemorrhage (ICH) is the most serious type of stroke, which results in a high disability or mortality rate. Therefore, accurate and rapid ICH region segmentation is of great significance for clinical diagnosis and treatment of ICH. In this paper, we focus on deep neural networks to automatically segment ICH regions. Firstly, we propose an encoder-decoder convolutional neural network (ED-Net) architecture to comprehensively utilizing both the low-level and high-level semantic information. Specifically, the encoder is used to extract multi-scale semantic feature information, while the decoder integrates them to form a unified ICH feature representation. Secondly, we introduce a synthetic loss function by paying more attention to the small ICH regions to overcome the data imbalanced problem. Thirdly, to improve the clinical adaptability of the proposed model, we collect 480 patient cases with ICH from four hospitals to construct a multi-center dataset, in which each case contains the first and review CT scans. In particular, CT scans of different patients are diverse, which greatly increases the difficulty of segmentation. Finally, we evaluate ED-Net on the multi-center ICH clinical dataset from different model parameters and different loss functions. We also compare the results of ED-Net with nine state-of-the-art methods in the literature. Both quantitative and visual results have shown that ED-Net outperforms other methods by providing more accurate and stable performance. 相似文献
11.
《Information processing & management》2023,60(2):103242
Among existing knowledge graph based question answering (KGQA) methods, relation supervision methods require labeled intermediate relations for stepwise reasoning. To avoid this enormous cost of labeling on large-scale knowledge graphs, weak supervision methods, which use only the answer entity to evaluate rewards as supervision, have been introduced. However, lacking intermediate supervision raises the issue of sparse rewards, which may result in two types of incorrect reasoning path: (1) incorrectly reasoned relations, even when the final answer entity may be correct; (2) correctly reasoned relations in a wrong order, which leads to an incorrect answer entity. To address these issues, this paper considers the multi-hop KGQA task as a Markov decision process, and proposes a model based on Reward Integration and Policy Evaluation (RIPE). In this model, an integrated reward function is designed to evaluate the reasoning process by leveraging both terminal and instant rewards. The intermediate supervision for each single reasoning hop is constructed with regard to both the fitness of the taken action and the evaluation of the unreasoned information remained in the updated question embeddings. In addition, to lead the agent to the answer entity along the correct reasoning path, an evaluation network is designed to evaluate the taken action in each hop. Extensive ablation studies and comparative experiments are conducted on four KGQA benchmark datasets. The results demonstrate that the proposed model outperforms the state-of-the-art approaches in terms of answering accuracy. 相似文献
12.
图书馆智能化咨询问答机器人是图书馆智能化机器人中的一种重要类型,系统设计是研究的首要内容,语料技术则是其服务效能的核心要素。本文从图书馆智能化咨询问答机器人的系统设计方案、问题语料库和答案语料库的建设与来源、分类类型、语料问题的分类与扩展、个性化分析与处理等方面,对图书馆智能化咨询问答机器人系统设计与语料技术进行了研究。此项研究对于图书馆智能化咨询问答机器人的全面研究具有参考和借鉴意义。 相似文献
13.
《Information processing & management》2023,60(2):103207
Visual Question Answering (VQA) requires reasoning about the visually-grounded relations in the image and question context. A crucial aspect of solving complex questions is reliable multi-hop reasoning, i.e., dynamically learning the interplay between visual entities in each step. In this paper, we investigate the potential of the reasoning graph network on multi-hop reasoning questions, especially over 3 “hops.” We call this model QMRGT: A Question-Guided Multi-hop Reasoning Graph Network. It constructs a cross-modal interaction module (CIM) and a multi-hop reasoning graph network (MRGT) and infers an answer by dynamically updating the inter-associated instruction between two modalities. Our graph reasoning module can apply to any multi-modal model. The experiments on VQA 2.0 and GQA (in fully supervised and O.O.D settings) datasets show that both QMRGT and pre-training V&L models+MRGT lead to improvement on visual question answering tasks. Graph-based multi-hop reasoning provides an effective signal for the visual question answering challenge, both for the O.O.D and high-level reasoning questions. 相似文献
14.
15.
《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
16.
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
17.
[目的/意义]实体语义关系分类是信息抽取重要任务之一，将非结构化文本转化成结构化知识，是构建领域本体、知识图谱、开发问答系统、信息检索系统的基础工作。[方法/过程]本文详细梳理了实体语义关系分类的发展历程，从技术方法、应用领域两方面回顾和总结了近5年国内外的最新研究成果，并指出了研究的不足及未来的研究方向。[结果/结论]热门的深度学习方法抛弃了传统浅层机器学习方法繁琐的特征工程，自动学习文本特征，实验发现，在神经网络模型中融入词法、句法特征、引入注意力机制能有效提升关系分类性能。 相似文献
18.
Community question answering (CQA) services that enable users to ask and answer questions have become popular on the internet. However, lots of new questions usually cannot be resolved by appropriate answerers effectively. To address this question routing task, in this paper, we treat it as a ranking problem and rank the potential answerers by the probability that they are able to solve the given new question. We utilize tensor model and topic model simultaneously to extract latent semantic relations among asker, question and answerer. Then, we propose a learning procedure based on the above models to get optimal ranking of answerers for new questions by optimizing the multi-class AUC (Area Under the ROC Curve). Experimental results on two real-world CQA datasets show that the proposed method is able to predict appropriate answerers for new questions and outperforms other state-of-the-art approaches. 相似文献
19.
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
20.
Wanpeng Song Liu Wenyin Naijie Gu Xiaojun Quan Tianyong Hao 《Information processing & management》2011
Question categorization, which suggests one of a set of predefined categories to a user’s question according to the question’s topic or content, is a useful technique in user-interactive question answering systems. In this paper, we propose an automatic method for question categorization in a user-interactive question answering system. This method includes four steps: feature space construction, topic-wise words identification and weighting, semantic mapping, and similarity calculation. We firstly construct the feature space based on all accumulated questions and calculate the feature vector of each predefined category which contains certain accumulated questions. When a new question is posted, the semantic pattern of the question is used to identify and weigh the important words of the question. After that, the question is semantically mapped into the constructed feature space to enrich its representation. Finally, the similarity between the question and each category is calculated based on their feature vectors. The category with the highest similarity is assigned to the question. The experimental results show that our proposed method achieves good categorization precision and outperforms the traditional categorization methods on the selected test questions. 相似文献