![]() ![]() We create a dataset for the application domain and make it publicly available. Our proposed solution can be used as one tap or one click solution for responding to various types of queries raised by faculty members and students in a university. We propose an application and solution approach for automatically generating and suggesting short email responses to support queries in a university environment. Responding to every query by manually typing is a tedious and time consuming task and an automated approach for email response suggestion can save lot of time. A large percentage of emails received by these departments are frequent and commonly used queries or request for information. Office of Academic Affairs (OAA), Office of Student Life (OSL) and Information Technology Helpdesk (ITD) are support functions within a university which receives hundreds of email messages on the daily basis. En este trabajo, se describen las acciones aborda-das en el proyecto de investigación "Clasificación automática de correos electrónicos", así como las líneas de I+D comprendidas en el mismo.īackground. A su vez, el correo electrónico posee características particulares respecto de otros elementos de texto que hace que existan diferencias y problemáticas particulares entre la minería de textos tradicional y la minería de correos electrónicos, conocida como email mining. En este sentido, existe un área particular del aprendizaje automático, denominada minería de tex-tos, donde el conocimiento es generado a partir de la adopción de bases de datos exclusivamente textuales como fuente de datos. En el último tiempo, con el objetivo de mejorar su uso y aprovechar a los correos electrónicos como fuente de conocimiento, se han aplicado diversas técnicas de aprendizaje automático a este tipo de información. Improved algorithm is functionally automated with machine learning techniques to assist email users who find it difficult to manage bulk variety of emails.Įl correo electrónico es una de las herramientas de comunicación asincrónica más extendidas en la actualidad, habiendo desplazado a los canales más clásicos de comunicación debido a su alta eficiencia, costo extremadamente bajo y compatibilidad con muchos tipos diferentes de información. It is observed that NLP techniques improve performance of Intelligent Email Reply algorithm enhancing its ability to classify and generate email responses with minimal errors using probabilistic methods. The open hypothesis of this research is that the underlying concept to fan email is communicating a message in form of text. An enhancement is presented in this research to address email management issues by incorporating optimized information extraction for email classification along with generating relevant dictionaries as emails vary in categories and increases in volume. Natural Language Processing (NLP) possess potential in optimizing text classification due to its direct relation with language structure. Still redundant information can cause errors in classifying an email. This helps in correct selection of template for email reply. ![]() Intelligent reply algorithms can be employed in which machine learning methods can accommodate email content using probabilistic methods to classify context and nature of email. Relevance characteristics defining class of email in general includes the topic of thee mail and the sender of the email along with the body of email. Email based communication over the course of globalization in recent years has transformed into an all-encompassing form of interaction and requires automatic processes to control email correspondence in an environment of increasing email database. ![]()
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