textual retrieval

[ˈtɛkstʃuəl rɪˈtrivəl][ˈtekstʃuəl rɪˈtri:vəl]

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  • The main purpose that introduces the granularity thinking of human beings in the textual case retrieval is to reduce the computational complexity get better retrieval efficiency and improve the retrieval speed .

    本文引入人类这种粒度的思想,主要目标就是根据 文本案例 检索存在的问题,结合粒度计算的思想,以降低计算的复杂度,得到最佳的检索效率,并提高检索的速度。

  • With the mechanism of textual entailment the performance of various natural language processing tasks will be improved including information extraction information retrieval document summarization question answering and text-to-scene conversion system .

    掌握发现蕴涵的机制,将可促进多方面自然语言处理任务的发展,如信息抽取、信息 检索、文档摘要、问答系统、 文景转换等。

  • Efficient technology of image retrieval can greatly help people obtain entertainment on Internet and enhance the quality of lives . Nowadays textual retrieval performs well .

    高效的图像检索技术能够极大地有助于人们在互联网上进行 数字娱乐,提高人们的生活品质。

  • Improving the efficiency of Recognizing Textual Entailment plays an important role in Information Retrieval Information Extraction Question Answering and Summarization .

    提高 文本蕴含识别的性能对于提高信息 检索、信息抽取、自动问答、文本摘要等系统的效率起到重要作用。

  • The paper describes the problem of textual case based retrieval with quotient space theory and divides the processes of textual case base retrieval in to dividing the quotient space for user text case and searching .

    本文用粒度计算的商空间理论描述 文本案例 检索问题,将文本案例检索分为为用户文本案例划分商空间和文本案例检索两个过程。

  • So we use textual CBR ( a embranchment of CBR ) to build case retrieval and learning module .

    针对这些特性,我们采用CBR的一个分支:基于 文本的案例推理( TextualCBR,TCBR)来完成推理和学习系统的构建。

  • Content-based image retrieval ( CBIR ) is a hotspot of image retrieval at computer filed . It is different from traditional textual retrieval .

    基于内容的图像检索是目前计算机图像检索研究的热点,它不同于传统的 文本 检索,因此将其应用于医学图像领域具有重要意义。

  • Dividing the case base or cases into different granularity whit different size to improve the speed and efficiency of dividing the quotient space for user textual case and the retrieval accuracy and reduce the retrieval time-consuming .

    将案例库划分为不同的粒度,以提高为用户 文本案例划分商空间的速度和效率,同时 提高 检索的准确率,降低检索的时间耗费。通过实验验证, 基于粒度的方法具有较好 检索效果。