Information Extraction From User Generated Noisy Texts

Information Extraction From User Generated Noisy Texts

Bibliographic Details
Main Author: Tabassum Binte Jafar, Jeniya
Language:English
Published: The Ohio State University / OhioLINK 2020
Subjects:
Artificial Intelligence
Computer Engineering
Computer Science
Linguistics
Natural Language Processing
Information Retrieval
Machine Learning
Social Media Analysis
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=osu1606315356821532
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-osu16063153568215322021-09-02T05:10:39Z Information Extraction From User Generated Noisy Texts Tabassum Binte Jafar, Jeniya Artificial Intelligence Computer Engineering Computer Science Linguistics Natural Language Processing Information Retrieval Machine Learning Social Media Analysis Social media websites provide an ideal environment for people to express their experiences on the latest events and share their knowledge about the current technologies along with research advancements. This presents an opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to facilitate large scale data-analysis applications by extracting machine-processable information from user generated unstructured texts. However, information extraction from social media is particularly challenging due to the inherent noise induced by different writing styles of its users and their writing errors such as: typos and non-grammatical sentences. In this thesis, we explore the supervised and semi-supervised approaches to extract structured information from the noisy user generated texts of three widely used social web spaces: Twitter, StackOverflow and ProtocolIO. 2020 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1606315356821532 http://rave.ohiolink.edu/etdc/view?acc_num=osu1606315356821532 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Artificial Intelligence
Computer Engineering
Computer Science
Linguistics
Natural Language Processing
Information Retrieval
Machine Learning
Social Media Analysis
spellingShingle Artificial Intelligence
Computer Engineering
Computer Science
Linguistics
Natural Language Processing
Information Retrieval
Machine Learning
Social Media Analysis
Tabassum Binte Jafar, Jeniya
Information Extraction From User Generated Noisy Texts
author Tabassum Binte Jafar, Jeniya
author_facet Tabassum Binte Jafar, Jeniya
author_sort Tabassum Binte Jafar, Jeniya
title Information Extraction From User Generated Noisy Texts
title_short Information Extraction From User Generated Noisy Texts
title_full Information Extraction From User Generated Noisy Texts
title_fullStr Information Extraction From User Generated Noisy Texts
title_full_unstemmed Information Extraction From User Generated Noisy Texts
title_sort information extraction from user generated noisy texts
publisher The Ohio State University / OhioLINK
publishDate 2020
url http://rave.ohiolink.edu/etdc/view?acc_num=osu1606315356821532
work_keys_str_mv AT tabassumbintejafarjeniya informationextractionfromusergeneratednoisytexts
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