Gathering Knowledge for Question Answering Beyond Named Entities

被引：0

作者：

Przybyla, Piotr ^{[1
]}

机构：

[1] Polish Acad Sci, Inst Comp Sci, PL-00901 Warsaw, Poland

来源：

NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS, NLDB 2015 | 2015年 / 9103卷

关键词：

D O I：

10.1007/978-3-319-19581-0_39

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an entity recognition (ER) module for a question answering system for Polish called RAFAEL. Two techniques of ER are compared: traditional, based on named entity categories (e.g. person), and novel Deep Entity Recognition, using WordNet synsets (e.g. impressionist). The latter is possible thanks to a previously assembled entity library, gathered by analysing encyclopaedia definitions. Evaluation based on over 500 questions answered on the grounds of Wikipedia suggests that the strength of DeepER approach lies in its ability to tackle questions that demand answers beyond the categories of named entities.

引用

页码：412 / 417

页数：6

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