With the rapid development of artificial intelligence technology, text categorization technology is becoming more and more mature. However, text categorization in real situations still faces various unconstrained conditions. English text is an important part of text information, it is also an important way for people to get information from abroad. How can everyone get the desired content from the massive data quickly and accurately, it has become a hot issue in current research. This paper improves the current text categorization algorithm based on English quality-related text categorization. The design and implementation of text categorization system are illustrated with an example of English quality-related text categorization system, complete the research work of text categorization algorithm. The core work of this paper is to mine, classify and analyze large amounts of data in English text by using the method of combining cyclic neural network with quality. Finally, the essential features of high quality English texts are obtained. Traditional English text categorization algorithm if the amount of training data is large, it is easy to show some defects such as unclear feature items. In view of these problems, in order to improve the accuracy and flexibility of English text categorization, this paper proposes a quality-related English text categorization method based on cyclic neural network. A mechanism combining attention is proposed to improve the problem of label disorder and make the structure of the model more flexible. The model proposed in this paper is compared and optimized. Experiments show that the accuracy of neural text classification based on quality classification can reach about 96%. 相似文献
With the rapid growth of inquiry in biomedicine concerning diseases, the recognition of diseases becomes especially important. But only the recognition of the biomedical concepts in literature is not enough, annotations and normalizations of the concepts with normalized Metathesaurus get even more important. This paper proposes a system to annotate the literature with normalized Metathesaurus. First, a two-phase Conditional random fields (CRFs) is used to recognize the disease mentions, including the location and identification. Then, the paper adapts the Disease ontology (DO) to annotate the diseases recognized for normalization by computing the similarity between disease mentions and concepts. According to the similarities, the disease mentions are denoted as disease concepts and instances distinctively. The experiments carried out on the Arizona disease corpus show that our system makes a good achievement and outperforms the other works. 相似文献
Bio-entity name recognition is the key step for information extraction from biomedical literature. This paper presents a dictionary-based bio-entity name recognition approach. The approach expands the bio-entity name dictionary via the Abbreviation Definitions identifying algorithm, improves the recall rate through the improved edit distance algorithm and adopts some post-processing methods including Pre-keyword and Post-keyword expansion, Part of Speech expansion, merge of adjacent bio-entity names and the exploitation of the contextual cues to further improve the performance. Experiment results show that with this approach even an internal dictionary-based system could achieve a fairly good performance. 相似文献
The paper describes a texture-based fast text location scheme which operates directly in the Discrete Wavelet Transform (DWT) domain. By the distinguishing texture characteristics encoded in wavelet transform domain, the text is fast detected from complex background images stored in the compressed format such as JPEG2000 without full decompress. Compared with some traditional character location methods, the proposed scheme has the advantages of low computational cost, robust to size and font of characters and high accuracy. Preliminary experimental results show that the proposed scheme is efficient and effective. 相似文献
New text indexing functionalities of the compressed suffix arrays are proposed. The compressed suffix array proposed by Grossi and Vitter is a space-efficient data structure for text indexing. It occupies only O(n) bits for a text of length n; however it also uses the text itself that occupies
bits for the alphabet
. In this paper we modify the data structure so that pattern matching can be done without any access to the text. In addition to the original functions of the compressed suffix array, we add new operations search, decompress and inverse to the compressed suffix arrays. We show that the new index can find occ occurrences of any substring P of the text in O(|P|logn+occlogεn) time for any fixed 1ε>0 without access to the text. The index also can decompress a part of the text of length m in O(m+logεn) time. For a text of length n on an alphabet
such that
, our new index occupies only
bits where
is the order-0 entropy of the text. Especially for ε=1 the size is
bits. Therefore the index will be smaller than the text, which means we can perform fast queries from compressed texts. 相似文献