【摘要】：使用中文文本挖掘方法來分析中國高校網(wǎng)頁中各專業(yè)培養(yǎng)方案和培養(yǎng)目標的非結(jié)構(gòu)化數(shù)據(jù)集。以K-means文本聚類算法和聚類結(jié)果歸納的各專業(yè)類別的技能關(guān)鍵詞為基礎(chǔ),在集成了所有專業(yè)領(lǐng)域的專有特征和專家審核并結(jié)合了頻率計算方法后,定義了技能指標與相應(yīng)各個專業(yè)的重要性程度。最后,建立了專業(yè)和技能之間的關(guān)聯(lián)知識庫,為構(gòu)建網(wǎng)絡(luò)化創(chuàng)新外包人才技能模型建立了基礎(chǔ)。通過實驗評估發(fā)現(xiàn),與基于基本中文語料庫的分詞方法相比較,在中文分詞過程中引入專業(yè)專有特征的方法能夠提供更加精確和合理的聚類結(jié)果。因此,本文提出的方法能夠高效地構(gòu)建專業(yè)技能關(guān)聯(lián)知識庫。
[Abstract]:The Chinese text mining method is used to analyze the unstructured data sets of the training programs and objectives of each major in the web pages of Chinese colleges and universities. Based on the K-means text clustering algorithm and the skill keywords of each specialty category summarized by the clustering results, after integrating the specific features of all specialized fields and expert audit and combining the frequency calculation method, The skill index and the importance of each major are defined. Finally, the related knowledge base between specialty and skill is established, which is the foundation of constructing network innovation outsourcing talent skill model. The experimental results show that compared with the word segmentation method based on the basic Chinese corpus, the method of introducing specialized and exclusive features in the process of Chinese word segmentation can provide more accurate and reasonable clustering results. Therefore, the method proposed in this paper can efficiently construct the knowledge base of professional skills association.
【作者單位】： 上海交通大學(xué)安泰經(jīng)濟與管理學(xué)院;上海大學(xué)管理學(xué)院;
【基金】：國家自然科學(xué)基金青年項目(71301102);國家自然科學(xué)基金資助項目(71171131) 國家自然科學(xué)基金委創(chuàng)新研究群體資助項目(71421002) 長江學(xué)者和創(chuàng)新團隊發(fā)展計劃資助項目(IRT13030)
【分類號】：G250.74;TP391.1
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本文編號：2368808