本文選題：詞性標(biāo)注 + 先秦古籍�。� 參考：《圖書(shū)情報(bào)工作》2017年12期

【摘要】：[目的 /意義]先秦典籍在古代典籍中的地位極為重要。本文提出對(duì)先秦典籍進(jìn)行詞性自動(dòng)標(biāo)注的解決方法,以便更加準(zhǔn)確地挖掘先秦典籍中的潛在知識(shí)。[方法 /過(guò)程]通過(guò)條件隨機(jī)場(chǎng)模型,結(jié)合統(tǒng)計(jì)方法確定組合特征模板,并最終得到針對(duì)先秦典籍的詞性自動(dòng)標(biāo)注算法模型。[結(jié)果 /結(jié)論]在先秦典籍自動(dòng)分詞的整個(gè)流程基礎(chǔ)上,得到簡(jiǎn)單特征模板、組合特征模板下的詞性自動(dòng)標(biāo)注模型,基于組合特征模板的詞性標(biāo)注模型調(diào)和平均值F達(dá)到94.79%,具有較強(qiáng)的推廣和應(yīng)用價(jià)值。在構(gòu)建詞性自動(dòng)標(biāo)注模型的過(guò)程中,通過(guò)融入字詞結(jié)構(gòu)、詞語(yǔ)拼音和字詞長(zhǎng)度的特征知識(shí),使得模型的精確率和召回率得到有效提升。
[Abstract]:The purpose / significance of the pre-Qin classics in ancient books is extremely important. This paper puts forward the solution of automatic marking of part of speech in pre-Qin books in order to excavate the potential knowledge of pre-Qin books more accurately. [method / process] through conditional random field model, combined with statistical method to determine the combination feature template, and finally to obtain the algorithm model of automatic tagging of part of speech for pre-Qin books. [results / conclusion] on the basis of the whole process of automatic word segmentation in pre-Qin classical books, a simple feature template and a part of speech automatic tagging model under combination feature template are obtained. The concordant average F of part of speech tagging model based on combined feature template is 94.79, which is worth popularizing and applying. In the process of constructing the automatic tagging model of part of speech, the precision and recall rate of the model can be improved effectively by incorporating the feature knowledge of word structure, word pinyin and word length.
【作者單位】： 南京農(nóng)業(yè)大學(xué)信息科學(xué)技術(shù)學(xué)院;南京農(nóng)業(yè)大學(xué)領(lǐng)域知識(shí)關(guān)聯(lián)研究中心;
【基金】：國(guó)家社會(huì)科學(xué)基金重大項(xiàng)目“基于《漢學(xué)引得叢刊》的典籍知識(shí)庫(kù)構(gòu)建及人文計(jì)算研究”(項(xiàng)目編號(hào):15ZDB127);國(guó)家社會(huì)科學(xué)基金青年項(xiàng)目“哈佛燕京學(xué)社漢學(xué)引得叢刊研究”(項(xiàng)目編號(hào):12CTQ019)研究成果之一 南京農(nóng)業(yè)大學(xué)人文社會(huì)科學(xué)基金項(xiàng)目(項(xiàng)目編號(hào):SKPT2016001)
【分類(lèi)號(hào)】：TP391.1
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本文編號(hào)：1840951