Detecting Contextual Event information in Emails

A powerful tool for planning and announcement of Events is Email. Automatic detection of the Occurrence (Title) and its contextual information (Location, Temporal information, Participants) associated with the email is surely desirable to help the users manage and plan important Events. A lot of work has been done in the area of Event detection but it has various limitations from different perspectives. Firstly, the existing work mainly targets text streams like news stories, scientific documents, articles etc that are somewhat structured documents with sufficient event description as compare to the Emails that have structured, semi-structured and unstructured short descriptions with a plenty of description styles. Secondly the objective in most of the research is to detect new or hot events. Thirdly, much of the existing work aims on reporting events and our objective is to support Event Planning and Management. Another lacking thing is the use of publication time as the temporal information instead of actual temporal information contained within text that is indeed required for Event planning and management task. We have used Finite State Automata (FSA) to extract phrases revealing the Places, temporal information and the actual occurrence. The results are evaluated using different measures. Experiments show that the proposed approach performed well on the Email data Corpus.

A powerful tool for planning and announcement of Events is Email. Automatic detection of the Occurrence (Title) and its contextual information (Location, Temporal information, Participants) associated with the email is surely desirable to help the users manage and plan important Events. A lot of work has been done in the area of Event detection but it has various limitations from different perspectives. Firstly, the existing work mainly targets text streams like news stories, scientific documents, articles etc that are somewhat structured documents with sufficient event description as compare to the Emails that have structured, semi-structured and unstructured short descriptions with a plenty of description styles. Secondly the objective in most of the research is to detect new or hot events. Thirdly, much of the existing work aims on reporting events and our objective is to support Event Planning and Management. Another lacking thing is the use of publication time as the temporal information instead of actual temporal information contained within text that is indeed required for Event planning and management task. We have used Finite State Automata (FSA) to extract phrases revealing the Places, temporal information and the actual occurrence. The results are evaluated using different measures. Experiments show that the proposed approach performed well on the Email data Corpus.

2020

This is an era where people are too busy to check out their inbox. They swipe off notifications, not knowing they might miss something important. These could be anything like, personal messages asking whether you are free to meet up or reminders to events that you need to attend or some emails regarding interesting events that are happening around you. The need of the hour is an efficient way for keeping track of the important events from the vast number of incoming messages. This paper proposes a solution based on event extraction from emails. The scope of the project is limited to Gmail. The primary component is an android application that identifies event containing emails, extracts important details and provides an automated reminder system for the same depending on user needs. Various machine learning techniques along with natural language processing are used for the fulfillment for this project.

2019

Most recently, with the advanced technological facilities, the automated techniques for extraction of event information has got significantly more importance; and stands as one of the most desirable tasks in the social text stream processing. Among the social text streams: email is one of the most broadly used methods for the official announcements. Moreover, emails have a very complex and diverse unbounded layout in all formats of text structures. In this paper, a novel technique is proposed for event extraction from the email text, where the definition that term "event" engages something as an occurrence or happening with specific attributes, such as at a particular location, date and time, involving one or more actors and participants. Existing work on event detection shows that people have partially represented their attributes. Mostly they have worked on the use of publication time as the temporal information instead of actual temporal information contained within the text. In this work, NLP techniques along with handwritten rules, word semantic tools like WordNet, and gazetteer lists are entailed for countering various issues in running text; it includes the requisite demands such as the grammatical structure of the sentence should be correct for revealing the boundary of the accurate phrase. The detailed evaluation of the proposed methodology is done with metrics metricizes like precision, recall, F1-measure. We are hopeful that researchers and professionals all around the worlds will employ the proposed method for event extraction.

2005

Abstract New Event Detection (NED) involves monitoring chronologically-ordered news streams to automatically detect the stories that report on new events. We compare two stories by finding three cosine similarities based on names, topics and the full text. These additional comparisons suggest treating the NED problem as a binary classification problem with the comparison scores serving as features.

2004

Abstract New Event Detection is a challenging task that still offers scope for great improvement after years of effort. In this paper we show how performance on New Event Detection (NED) can be improved by the use of text classification techniques as well as by using named entities in a new way. We explore modifications to the document representation in a vector space-based NED system. We also show that addressing named entities preferentially is useful only in certain situations.

Ingénierie des systèmes d'information, 2016

Social media systems have been proven to be valuable platforms for information and communication, particularly during events; in case of natural disaster like earthquakes tsunami and states of nuclear emergencies in Japan in 2011. The behavior leads to an accumulation of an enormous amount of information. However, finding relevant posts can be a challenging task, since the relevance of a post is dependent both on its content, author and tweet's characteristics. Besides identifying tweets that describe a specific type of event is also challenging due to the high complexity and variety of event descriptions. These challenges present a big opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to enable new large-scale data-analysis applications. Taking to account all the difficulties, this paper proposes a new metric to improve the results of the searches in microblogs. It combines content relevance, tweet relevance and author relevance, and develops a Natural Language Processing method for extracting temporal information of events from posts more specifically tweets. Our approach is based on a methodology of temporal markers classes and on a contextual exploration method. To evaluate our model, we built a knowledge management system. Actually, we used a collection of 10 thousand of tweets talking about the current events in 2014 and 2015.

International Journal of Informatics and Communication Technology, 2022

Online event detection (OED) has seen a rise in the research community as it can provide quick identification of possible events happening at times in the world. Through these systems, potential events can be indicated well before they are reported by the news media, by grouping similar documents shared over social media by users. Most OED systems use textual similarities for this purpose. Similar documents, that may indicate a potential event, are further strengthened by the replies made by other users, thereby improving the potentiality of the group. However, these documents are at times unusable as independent documents, as they may replace previously appeared noun phrases with pronouns, leading OED systems to fail while grouping these replies to their suitable clusters. In this paper, a pronoun resolution system that tries to replace pronouns with relevant nouns over social media data is proposed. Results show significant improvement in performance using the proposed system.

2016

103 Event detection in Tweets Andrei-Bogdan Baran “Alexandru Ioan Cuza” University, Faculty of Computer Science General Berthelot, No. 16 [email protected] Adrian Iftene “Alexandru Ioan Cuza” University, Faculty of Computer Science General Berthelot, No. 16 [email protected] ABSTRACT Twitter is among the fastest-growing online social networking services, with more than 140 million users producing over 400 million tweets per day. It enables users to post status updates (tweets) about a huge variety of topics to a network of followers using various communication services such as cell phones, e-mails, Web interfaces, or other third-party applications. Monitoring and analyzing this rich and continuous usergenerated content can lead to obtaining valuable information about local and global news and events, because virtually, any person witnessing or involved in any event is nowadays able to disseminate realtime information, which can reach the other side of the world as the ev...

2022

People post information about different topics which are in their active vocabulary over social media platforms (like Twitter, Facebook, PInterest and Google+). They follow each other and it is more likely that the person who posts information about current happenings will receive better response. Manual analysis of huge amount of data on social media platforms is difficult. This has opened new research directions for automatic analysis of usercontributed social media documents. Automatic social media data analysis is difficult due to abundant information shared by users. Many researchers use Twitter data for Social Media Analysis (SMA) as the Twitter data is freely available in the public domain. One of the most this research work. Event Detection from social media data is used for different applications like traffic congestion detection, disaster and emergency management, and live news detection. Nature of the information which is shared on twitter platform is short-text, noisy, and ambiguous. Thus, event detection and extraction of event phrases from user-generated and ill-I extend my thanks to my guide Dr. Mukesh Kumar,