SENSEI
Making Sense of Human-Human Conversation Data
| Coordinatore | UNIVERSITA DEGLI STUDI DI TRENTO
Organization address
address: Via Sommarive 9 contact info |
| Nazionalità Coordinatore | Italy [IT] |
| Totale costo | 3˙560˙044 € |
| EC contributo | 2˙650˙000 € |
| Programma | FP7-ICT
Specific Programme "Cooperation": Information and communication technologies |
| Code Call | FP7-ICT-2013-10 |
| Funding Scheme | CP |
| Anno di inizio | 2013 |
| Periodo (anno-mese-giorno) | 2013-11-01 - 2016-10-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
UNIVERSITA DEGLI STUDI DI TRENTO
Organization address
address: Via Sommarive 9 contact info |
IT (Povo, Trento) | coordinator | 0.00 |
| 2 |
IN & OUT SPA CON SOCIO UNICO
Organization address
address: VIA DI PRISCILLA 101 contact info |
IT (ROMA) | participant | 0.00 |
| 3 |
THE UNIVERSITY OF SHEFFIELD
Organization address
address: FIRTH COURT WESTERN BANK contact info |
UK (SHEFFIELD) | participant | 0.00 |
| 4 |
UNIVERSITE D'AIX MARSEILLE
Organization address
address: Boulevard Charles Livon 58 contact info |
FR (Marseille) | participant | 0.00 |
| 5 |
UNIVERSITY OF ESSEX
Organization address
address: WIVENHOE PARK contact info |
UK (COLCHESTER) | participant | 0.00 |
| 6 |
WEBSAYS SL
Organization address
address: Calle Napoles, Planta 7, Puerta 4 294 contact info |
ES (BARCELONA) | participant | 0.00 |
Mappa
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Obiettivo del progetto (Objective)
The overall goals of the SENSEI project are twofold. First, SENSEI will develop summarization/analytics technology to help users make sense of human conversation streams from diverse media channels. Second, SENSEI will design and evaluate its summarization technology in ecological environments, aiming to improve task performance and productivity of end-users.Conversational interaction is the most natural and persistent paradigm for business relations with end-customers or users. In contact centres millions of customer spoken conversations are handled daily. On social media platforms hundreds of millions of blog posts are delivered through generalist or proprietary platforms. In both cases, conversations have little impact on the intended target 'listeners' due to the volume, velocity and diversity (media, style, social context) of the document streams (spoken conversations and blog posts). Most language analytics technology is limited in that it performs keyword search, which does not provide automatic descriptions of what happened, who said what, which opinions are held on what subject, in a coherent, readable and executable form. In the SENSEI project we plan to go beyond keyword search and sentence based analysis of conversations. We will design and adapt lightweight and large coverage linguistic models of semantic and discourse resources to learn a layered model of conversations. SENSEI will address the issue of multidimensional textual, spoken and metadata descriptors in terms of semantic, para-semantic and discourse structures. The combination of supervised and unsupervised learning techniques will support the scaling and adaptation of such computational models to the diversity of the conversation data. Automated generation of readable analytics documents (summaries) will support end-users in the context of large data analysis tasks. Summarization technology developed in SENSEI will be evaluated with respect to user's productivity in the context of ecological scenarios, specifically, call centre and social media conversation analysis.