Meeting Intents Detection Based on Ontology for Automatic Email Answering - Archive ouverte HAL Access content directly
Conference Papers Year :

Meeting Intents Detection Based on Ontology for Automatic Email Answering

(1) , (2) , (2)
1
2
Manon Cassier
  • Function : Author
Zied Sellami
  • Function : Author
  • PersonId : 1051321
Jean-Pierre Lorré
  • Function : Author
  • PersonId : 998553

Abstract

Automatic email answering is a difficult AI problem that combines classification, natural language understanding and text generation techniques. We present an original approach and a tool based on an ontology to automatically reply to meeting emails. We constructed the ontology from a French corpus of 1150 emails in which the concepts represent detailed meeting intents (proposing a meeting, cancelling a meeting, rescheduling a meeting) and different answer templates. Each intent concept is a semantic rule formalized according to the FrameNet methodology. These rules are used to detect intents in emails and also to extract relevant information (such as date, time or person) used for generating replies. The main advantage of our approach is the generation of more precise answers than those proposed by other approaches. We tested the intent detection step on a set of 297 emails and compared it with different supervised machine learning algorithms. Obtained results are encouraging, with an accuracy 20% higher than results obtained with other algorithms. keywords : Ontology engineering, knowledge acquisition from text, knowledge-based recommendation systems.
Fichier principal
Vignette du fichier
IC2019_FinalPaper.pdf (403.49 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02192448 , version 1 (23-07-2019)

Identifiers

  • HAL Id : hal-02192448 , version 1

Cite

Manon Cassier, Zied Sellami, Jean-Pierre Lorré. Meeting Intents Detection Based on Ontology for Automatic Email Answering. Journées francophones d'Ingénierie des Connaissances (IC 2019), Jul 2019, Toulouse, France. ⟨hal-02192448⟩
36 View
247 Download

Share

Gmail Facebook Twitter LinkedIn More