Identifying Business Tasks and Commitments from Email and Chat Conversations
Abstract
Case management applications and, more generally, people-centric processes involve the definition, resolution and communication of commitments for tasks over channels such as chat and email. Identifying and tracking tasks and commitments can help in streamlining the collaborative work in business environments. However, doing so proves challenging due to the syntactical, grammatical, and structural incompleteness of human conversations over chat and email channels. We present a novel approach to automatically identify tasks and commitment creation, delegation, completion, and cancellation in email and chat conversations, based on techniques from natural language processing and machine learning domains. We discover tasks and related parameters from the text of conversations, identify when a commitment to a task emerges and find the state changes of a commitment based features extracted from the text of the conversations. We have developed a prototype and evaluated our approach using real-world chat and email datasets. Our experiments shows high precision for create class i.e., 90% in emails (Enron email corpus) and 80% in a real-world chat dataset and also provides promising results for discharge, delegate, and cancel classes.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 10, 2013
- Accession Number
- AD1142111
Entities
People
- Anup K. Kalia
- Claudio Bartolini
- Hamid-reza Motahari-nezhad
- Munindar P. Singh
Organizations
- Hp
- North Carolina State University