榫欐簮鏈熷垔缃?/p>
http://www.qikan.com.cn
浣滆�呮爣绛句富棰樻ā鍨嬪湪绉戞妧鏂囩尞涓殑搴旂敤
浣滆�咃細闄堟案鎭?/p>
宸︿竾鍒?/p>
鏋楄��杩?/p>
鏉ユ簮锛氥�婅绠楁満搴旂敤銆?/p>
2015
骞寸
04
鏈?/p>
鎽樿锛氫綔鑰呬富棰樻ā鍨嬭骞挎硾搴旂敤浜庣鎶�鏂囩尞涓綔鑰呯殑鍏磋叮鍙戠幇銆傞拡瀵逛綔鑰呬富棰樻ā鍨嬩笉鑳藉埄
鐢ㄦ枃鐚殑绫诲埆鏍囩灞炴�т笌涓婚涔嬮棿鐨勭浉鍏虫�ц繘琛屼富棰樺彂鐜扮殑闂锛屽湪瀵逛綔鑰呬富棰樻ā鍨嬪垎鏋愮殑鍩?/p>
纭�涓婏紝灏嗙鎶�鏂囩尞涔嬮棿鍥烘湁鐨勭被鍒爣绛句俊鎭紩鍏ュ埌浣滆�呬富棰樻ā鍨嬩腑锛屾彁鍑轰簡浣滆�呮爣绛句富棰?/p>
锛?/p>
LAT
锛夋ā鍨嬨�?/p>
LAT
妯″瀷閫氳繃瀹炵幇鏂囩尞鐨勬爣绛句俊鎭笌涓婚涔嬮棿鐨勬槧灏勫叧绯伙紝瀹炵幇鏂囨湰鐨勫鏍囩
鍒ゅ畾锛屾彁鍗囨枃妗ｇ殑鑱氱被鏁堟灉銆備笌浼犵粺鐨勬綔鍦ㄧ媱鍒╁厠闆峰垎閰嶏紙
LDA
锛夊拰浣滆�呬富棰橈紙
AT
锛夋ā鍨嬬殑
瀵规瘮瀹為獙缁撴灉琛ㄦ槑锛?/p>
LAT
妯″瀷鑳藉鏄捐憲鎻愰珮妯″瀷鐨勬硾鍖栬兘鍔涳紝鎻愬崌妯″瀷鐨勮繍琛屾�ц兘銆?/p>
鍏抽敭璇嶏細鏂囧瓧淇℃伅澶勭悊锛涢殣钘忕媱鍒╁厠闆峰垎閰嶏紱鏂囨湰鍒嗘瀽锛涗富棰樻ā鍨嬶紱鑷劧璇█澶勭悊
涓浘鍒嗙被鍙凤細
TP181
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TP301.2
鏂囩尞鏍囧織鐮侊細
A
Abstract
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Author Topic
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AT
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model is widely used to find the authors interests in scientific
literature
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but AT model cannot take advantage of the correlation between category labels and topics.
Through integrating the inherent category labels of documents into AT model
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Labeled Author
Topic
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LAT
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model was proposed. LAT model realized the predicate of multilabels by optimizing
the mapping relation between labels and topics and improved the clustering results. The experimental
results suggest that
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compared with Latent Dirichlet Allocation
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LDA
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model and AT model
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LAT model can improve the decision accuracy of multilabels
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and optimize the generalization
ability and operating efficiency.
鑻辨枃鍏抽敭璇?/p>
Key words
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text information processing
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Latent Dirichlet Allocation
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LDA
锛夛紱
text analysis
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topic model
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natural language processing
0
寮曡█
绉戞妧鏂囩尞璧勬簮鐨勫埄鐢ㄩ暱鏈熶互鏉ュ彈鍒板鏈晫鐨勯噸瑙嗭紝鏃╂湡鐨勭爺绌舵槸鍒╃敤浼犵粺鐨勪俊鎭绱㈡柟娉?/p>
瀵圭鎶�鏂囩尞杩涜璁￠噺鍒嗘瀽銆佹彁渚涘叡浜湇鍔＄瓑
[1]
銆傝繖浜涗粎闈犺瘝棰戠粺璁＄殑鏂规硶闅句互鍙戠幇鏂囨。涓綔
鍦ㄧ殑璇箟鑱旂郴锛屾棤娉曟弧瓒崇敤鎴峰绉戞妧淇℃伅娣卞眰娆＄殑闇�姹?/p>
[2-3]
銆備汉浠湪鑰冭檻鏂囨湰鍜岃瘝涔嬮棿鍏宠仈鍏?/p>
绯荤殑鍩虹涓婏紝杩涗竴姝ュ鍏ヤ簡涓婚缁村害锛屼篃绉伴殣钘忚涔夌淮搴︼紝瀹炵幇浜嗘枃鏈湪闅愯棌璇箟灞傜殑鐩镐技搴?/p>
璁＄畻銆備负浜嗚嚜鍔ㄦ娊鍙栬繖绉嶉殣钘忚涔夌┖闂达紝瀹炵幇鏂囨湰鐨勪綆缁存綔鍦ㄨ涔夎〃绀猴紝浜х敓浜嗛殣钘忚涔夌储寮?/p>
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Latent Semantic Indexing
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LSI
锛夋ā鍨?/p>
[4]
銆備絾鏄?/p>
LSI
妯″瀷鍋囪鏂囨湰涓庝富棰樹箣闂存槸涓�瀵逛竴鐨勬槧
灏勫叧绯伙紝杩欑鏄犲皠鍏崇郴涓嶈兘灏嗘枃鏈槧灏勪负澶氫釜涓婚鐨勭粍鍚堛�傛鐜囨綔鍦ㄨ涔夌储寮曪紙
Probability
Latent Semantic Indexing
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PLSI
锛夌敤姒傜巼妯″瀷鐨勬柟寮忔妸鏂囨湰瀵瑰簲涓轰笉鍚屼富棰樼殑姒傜巼鍒嗗竷锛岃В鍐?/p>
浜嗘枃鏈笌涓婚涔嬮棿鏄竴瀵瑰鐨勬槧灏勬弿杩伴棶棰樸�備絾鏄紝
PLSI
姒傜巼妯″瀷骞朵笉鏄畬澶囩殑鐢熸垚寮忔ā