Cognitive Semiotic Model for Query Expansion in Question Answering

被引：0

作者：

Sirenko, Alexander ^{[1
,4
]}

Cherkasova, Galina ^{[2
,4
]}

Philippovich, Yuriy ^{[3
,4
]}

Karaulov, Yuriy ^{[3
,4
]}

机构：

[1] Moscow State Univ Printing Arts, Moscow, Russia

[2] Russian Acad Sci, Inst Linguist, Moscow, Russia

[3] Bauman Moscow State Tech Univ, Moscow, Russia

[4] Russian Acad Sci, VV Vinogradov Russian Language Inst, Moscow, Russia

来源：

ANALYSIS OF IMAGES, SOCIAL NETWORKS AND TEXTS | 2014年 / 436卷

关键词：

Semiotic modeling; Cognitive experiments; Grammar; Query expansion; Regression; Question answering;

D O I：

10.1007/978-3-319-12580-0_23

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Query expansion improves performance of informational retrieval stage in question answering pipeline. We state the benefits of a personalized and autonomous query preprocessing and automate a semiotic model to achieve such properties. The model operates as a context-sensitive weighted grammar, along with the algorithm to apply production rules allowing approximate matching. The semiotic model is packed into a regression model to predict relevant terms for a query. ROC-analysis evaluates the regression model and helps to choose the optimal cutoff level. We compare ranking of terms by regression model and ranking based on an external informational retrieval system.

引用

页码：222 / 228

页数：7

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