small系统中短信的自动主题识别与分类

ACM DEV '12 Pub Date : 2012-03-11 DOI:10.1145/2160601.2160626

Fahad Pervaiz, L. Subramanian, U. Saif

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引用次数: 0

摘要

本文提出了一种在SMS系统中识别主题和分类文本消息的方法，该系统是巴基斯坦的Twitter(除了通过SMS)。在众多挑战中，其中之一是开发一种无监督算法，用于包含以罗马字母书写的乌尔都语-英语单词的短信。在1克的情况下，流行话题、中等话题和稀有话题的真阳性率分别为72%、53%和58%，在2克和3克的情况下，真阳性率分别为48%和40%。

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Automatic topic identification and classification of text messages in the SMSAll system

This paper presents a way to identify topics and classify text messages in the SMSAll system, which is the Twitter of Pakistan (except over SMS). Among many challenges, one is to develop an unsupervised algorithm for text messages containing Urdu-English words written in roman letters. Still in 1-gram we are able to have 72%, 53% and 58% true positives for popular, medium and rare topics respectively and 48% and 40% true positives in 2 and 3-grams respectively.

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ACM DEV '12

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