Frame-Based Detection of Opinion Holders and Topics: A Model and a Tool

被引：90

作者：

Gangemi, Aldo ^{[1
,2
]}

Presutti, Valentina ^{[2
]}

Recupero, Diego Reforgiato ^{[2
]}

机构：

[1] Univ Paris 13, CNRS SorbonneCite, LIPN, F-93430 Villetaneuse, France

[2] ISTC CNR, STLab, Rome, Italy

来源：

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | 2014年 / 9卷 / 01期

关键词：

Evaluation results - Frame-based - Graph mining - NAtural language processing - Sub topics - Topic detection;

D O I：

10.1109/MCI.2013.2291688

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Sentilo is a model and a tool to detect holders and topics of opinion sentences. Sentilo implements an approach based on the neo-Davidsonian assumption that events and situations are the primary entities for contextualizing opinions, which makes it able to distinguish holders, main topics, and sub-topics of an opinion. It uses a heuristic graph mining approach that relies on FRED, a machine reader for the Semantic Web that leverages Natural Language Processing (NLP) and Knowledge Representation (KR) components jointly with cognitively-inspired frames. The evaluation results are excellent for holder detection (F1: 95%), very good for subtopic detection (F1: 78%), and good for topic detection (F1: 68%).

引用

页码：20 / 30

页数：11

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