IHS\_RD: Lexical Normalization for English Tweets

To What Extent Does Lexical Normalization Help English-as-a-Second Language Learners to Read Noisy English Texts?

How difficult is it for English-as-a-second language (ESL) learners to read noisy English texts? Do ESL learners need lexical normalization to read noisy English texts? These questions may also affect community formation…

Frustration Level Annotation in Latvian Tweets with Non-Lexical Means of Expression

We present a neural-network-driven model for annotating frustration intensity in customer support tweets, based on representing tweet texts using a bag-of-words encoding after processing with subword segmentation togethe…

Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data?

Social media is a valuable data resource for various natural language processing (NLP) tasks. However, standard NLP tools were often designed with standard texts in mind, and their performance decreases heavily when appl…

Towards Shared Datasets for Normalization Research

In this paper we present a Dutch and English dataset that can serve as a gold standard for evaluating text normalization approaches. With the combination of text messages, message board posts and tweets, these datasets r…

USZEGED: Correction Type-sensitive Normalization of English Tweets Using Efficiently Indexed n-gram Statistics