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261.
The vector space model (VSM) is a textual representation method that is widely used in documents classification. However, it remains to be a space-challenging problem. One attempt to alleviate the space problem is by using dimensionality reduction techniques, however, such techniques have deficiencies such as losing some important information. In this paper, we propose a novel text classification method that neither uses VSM nor dimensionality reduction techniques. The proposed method is a space efficient method that utilizes the first order Markov model for hierarchical Arabic text classification. For each category and sub-category, a Markov chain model is prepared based on the neighboring characters sequences. The prepared models are then used for scoring documents for classification purposes. For evaluation, we used a hierarchical Arabic text data collection that contains 11,191 documents that belong to eight topics distributed into 3-levels. The experimental results show that the Markov chains based method significantly outperforms the baseline system that employs the latent semantic indexing (LSI) method. That is, the proposed method enhances the F1-measure by 3.47%. The novelty of this work lies on the idea of decomposing words into sequences of characters, which found to be a promising approach in terms of space and accuracy. Based on our best knowledge, this is the first attempt to conduct research for hierarchical Arabic text classification with such relatively large data collection. 相似文献
262.
有效评估药物专利价值有必要考虑制药基础技术细节以及新药专利保护期限较长的特殊性等有关实际,同时,利用机器学习方法开展专利价值评估的研究仍有待进一步完善,因此,针对生物制药产业专利价值评估准确性问题,结合产业技术因素及其专利特点,以及专利价值评估的共性指标和生物制药产业特征与专利技术特点的个性指标,引入自编码器(AE)模型和谱聚类算法(SC)构建专利价值评估算法模型,以药智专利通数据库的相关专利数据为样本进行实证分析,通过提取专利指标特征、专利聚类来进行专利价值评估,并运用支持向量机方法对专利价值进行分类,以验证AE-SC评估模型的有效性。结果表明:AE-SC评估模型通过自编码器提取专利特征后的专利价值聚类准确度优于谱聚类和传统K-means聚类;专利存在年数、药物专利类型、适应证类别等是评价生物制药产业专利价值必要考虑因素。 相似文献
263.
《Information processing & management》2023,60(2):103202
Wikipedia links its articles by manually defined semantic relations called the Wikipedia hyperlink (link) structure. The existing Wikipedia link-based semantic similarity (SS) and semantic relatedness (SR) computation models, such as Wikipedia one-way link (WOLM) model and Wikipedia two-way link (WTLM) model, do not assess the strengths of the relationships between a candidate concept and its links (out-links or in-links). These models treat all the links as equally important even though some links are semantically more influential than others and should be given more importance. This phenomenon reduces the accuracy of these models. This paper presents the Wikipedia bi-linear link (WBLM) model that extends the previously proposed WOLM and WTLM models. The WBLM model explores the Wikipedia link structure as a semantic graph and discovers the strongly (bi-linear links) and weakly (out-links or in-links) connected links of a candidate concept. It improves the link-based vector representations of concepts by assigning weights to their connected links according to the strengths of their semantic associations. The experimental results demonstrate that the proposed WBLM model significantly improves the SS and SR computation accuracy of the WOLM model (6.9%, 8%, 24%, 17.3%, 31.2%, 30.6%, 26.5%, and 35.4%) and WTLM model (1.2%, 3.9%, 7.1%, 9.9%, 11%, 6.3%, 12.7%, and 13%), in terms of linear correlations with human judgments on gold standard benchmarks, including MC30, RG65, WS203, SimLex, 353All, MTurk287, MTurk771, and MEN3000, respectively. Moreover, this research offers a deep insight into the Wikipedia link structure and provides an adequate base for understanding it as a semantic graph. 相似文献
264.
《Information processing & management》2023,60(1):103168
Detection at an early stage is vital for the diagnosis of the majority of critical illnesses and is the same for identifying people suffering from depression. Nowadays, a number of researches have been done successfully to identify depressed persons based on their social media postings. However, an unexpected bias has been observed in these studies, which can be due to various factors like unequal data distribution. In this paper, the imbalance found in terms of participation in the various age groups and demographics is normalized using the one-shot decision approach. Further, we present an ensemble model combining SVM and KNN with the intrinsic explainability in conjunction with noisy label correction approaches, offering an innovative solution to the problem of distinguishing between depression symptoms and suicidal ideas. We achieved a final classification accuracy of 98.05%, with the proposed ensemble model ensuring that the data classification is not biased in any manner. 相似文献