tailieunhanh - Báo cáo khoa học: "NLP-based Tweet Search"

Tweets have become a comprehensive repository for real-time information. However, it is often hard for users to quickly get information they are interested in from tweets, owing to the sheer volume of tweets as well as their noisy and informal nature. We present QuickView, an NLP-based tweet search platform to tackle this issue. | QuickView NLP-based Tweet Search Xiaohua Liu t Furu Wei t Ming Zhou t Microsoft QuickView Team 1 School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China iMicrosoft Research Asia Beijing 100190 China 1 xiaoliu fuwei mingzhou qv @ Abstract Tweets have become a comprehensive repository for real-time information. However it is often hard for users to quickly get information they are interested in from tweets owing to the sheer volume of tweets as well as their noisy and informal nature. We present QuickView an NLP-based tweet search platform to tackle this issue. Specifically it exploits a series of natural language processing technologies such as tweet normalization named entity recognition semantic role labeling sentiment analysis tweet classification to extract useful information . named entities events opinions etc. from a large volume of tweets. Then non-noisy tweets together with the mined information are indexed on top of which two brand new scenarios are enabled . categorized browsing and advanced search allowing users to effectively access either the tweets or fine-grained information they are interested in. 1 Introduction Tweets represent a comprehensive fresh information repository. However users often have difficulty finding information they are interested in from tweets because of the huge number of tweets as well as their noisy and informal nature. Tweet search . Twitter 1 is a kind of service aiming to tackle this issue. Nevertheless existing tweet search services provide limited functionality. For example in Twitter only a simple keyword-based search is sup 1 http 13 ported and the returned list often contains meaningless results. This demonstration introduces QuickView which employs a series of NLP technologies to extract useful information from a large volume of tweets. Specifically for each tweet it first conducts normalization followed by named entity recognition NER . Then it .