| 第六章 總結(jié)和討論 |
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第6.1.節(jié) 總結(jié)信息抽取是近十年來新發(fā)展起來的領(lǐng)域。 MUC 等國際研討會(huì)給予高度關(guān)注，并提出了評(píng)價(jià)這類系統(tǒng)的方法，定義了評(píng)價(jià)指標(biāo)體系。 信息抽取技術(shù)的研究對象包括結(jié)構(gòu)化、半結(jié)構(gòu)化和自由式文檔。對于自由式文檔，多數(shù)采用了自然語言處理的方法，而其他兩類文檔的處理則多數(shù)是基于分隔符的。 網(wǎng)頁是信息抽取技術(shù)研究的重點(diǎn)之一。通常用分裝器從一特定網(wǎng)站上抽取信息。用一系列能處理不同網(wǎng)站的分裝器就能將數(shù)據(jù)統(tǒng)一表示，并獲得它們之間的關(guān)系。 分裝器的建造通常是費(fèi)事費(fèi)力的，而且需要專門知識(shí)。加上網(wǎng)頁動(dòng)態(tài)變化，維護(hù)分裝器的成本將很高。因此，如何自動(dòng)構(gòu)建分裝器便成為主要的問題。通常采用的方法包括基于歸納學(xué)習(xí)的機(jī)器學(xué)習(xí)方法。 有若干研究系統(tǒng)被開發(fā)出來。這些系統(tǒng)使用機(jī)器學(xué)習(xí)算法針對網(wǎng)上信息源生成抽取規(guī)則。 ShopBot ， WIEN ， SoftMealy 和 STALKER 生成的分裝器以分隔符為基礎(chǔ)，能處理結(jié)構(gòu)化程度高的網(wǎng)站。 RAPIER ， WHISK 和 SRV 能處理結(jié)構(gòu)化程度稍差的信息源。所采用的抽取方法與傳統(tǒng)的 IE 方法一脈相承，而學(xué)習(xí)算法多用關(guān)系學(xué)習(xí)法。 網(wǎng)站信息抽取和分裝器生成技術(shù)可在一系列的應(yīng)用領(lǐng)域內(nèi)發(fā)揮作用。目前只有比價(jià)購物方面的商業(yè)應(yīng)用比較成功，而最出色的系統(tǒng)包括 Jango ， Junglee 和 MySimon 。 第6.2.節(jié) 討論目前的搜索引擎并不能收集到網(wǎng)上數(shù)據(jù)庫內(nèi)的信息。根據(jù)用戶的查詢請求，搜索引擎能找到相關(guān)的網(wǎng)頁，但不能把上面的信息抽取出來。“暗藏網(wǎng)”不斷增加，因此有必要開發(fā)一些工具把相關(guān)信息從網(wǎng)頁上抽取并收集起來。 由于網(wǎng)上信息整合越來越重要，雖然網(wǎng)站信息抽取的研究比較新，但將不斷發(fā)展。機(jī)器學(xué)習(xí)方法的使用仍將成為主流方法，因?yàn)樘幚韯?dòng)態(tài)的海量信息需要自動(dòng)化程度高的技術(shù)。在文獻(xiàn) [52] 中提出，結(jié)合不同類型的方法，以開發(fā)出適應(yīng)性強(qiáng)的系統(tǒng)，這應(yīng)是一個(gè)有前途的方向。在文獻(xiàn) [36] 中，一種混合語言知識(shí)和句法特征的方法也被提出來。 本文介紹的系統(tǒng)多數(shù)是針對 HTML 文檔的。以后幾年 XML 的使用將被普及。 HTML 描述的是文檔的表現(xiàn)方式，是文檔的格式語言。 XML 則可以告訴你文檔的意義，即定義內(nèi)容而不只是形式。這雖然使分裝器的生成工作變得簡單，但不能排除其存在的必要性。 將來的挑戰(zhàn)是建造靈活和可升級(jí)的分裝器自動(dòng)歸納系統(tǒng)，以適應(yīng)不斷增長的動(dòng)態(tài)網(wǎng)絡(luò)的需要。 |
| 參考文獻(xiàn) |
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