Name
Abstract | ||
|---|---|---|
As social media constitutes a valuable source for data analysis for a wide range of applications, the need for handling such data arises. However, the nonstandard language used on social media poses problems for natural language processing (NLP) tools, as these are typically trained on standard language material. We propose a text normalization approach to tackle this problem. More specifically, we investigate the usefulness of a multimodular approach to account for the diversity of normalization issues encountered in user-generated content (UGC). We consider three different types of UGC written in Dutch (SNS, SMS, and tweets) and provide a detailed analysis of the performance of the different modules and the overall system. We also apply an extrinsic evaluation by evaluating the performance of a part-of-speech tagger, lemmatizer, and named-entity recognizer before and after normalization. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2850422 | ACM TIST |
Keywords | Field | DocType |
Social media,text normalization,user-generated content | User-generated content,Lemmatisation,Normalization (statistics),Social media,Standard language,Computer science,Machine translation,Natural language processing,Artificial intelligence,Text normalization,Machine learning | Journal |
Volume | Issue | ISSN |
7 | 4 | 2157-6904 |
Citations | PageRank | References |
4 | 0.38 | 43 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarah Schulz | 1 | 31 | 3.72 |
| Guy Pauw | 2 | 75 | 12.47 |
| Orphée De Clercq | 3 | 117 | 9.61 |
| Bart Desmet | 4 | 75 | 7.92 |
| Véronique Hoste | 5 | 319 | 35.92 |
| Walter Daelemans | 6 | 2019 | 269.73 |
| Lieve Macken | 7 | 51 | 10.81 |