Name
Abstract | ||
|---|---|---|
While speaking spontaneously, speakers often make errors such as self-correction or false starts which interfere with the successful application of natural language processing techniques like summarization and machine translation to this data. There is active work on reconstructing this errorful data into a clean and fluent transcript by identifying and removing these simple errors. Previous research has approximated the potential benefit of conducting word-level reconstruction of simple errors only on those sentences known to have errors. In this work, we explore new approaches for automatically identifying speaker construction errors on the utterance level, and quantify the impact that this initial step has on word- and sentence-level reconstruction accuracy. |
Year | Venue | Keywords |
|---|---|---|
2009 | EMNLP | natural language processing technique,initial step,false start,word-level error identification,disfluency correction,word-level reconstruction,sentence-level reconstruction accuracy,machine translation,fluent transcript,active work,integrating sentence,errorful data,simple error,natural language processing |
Field | DocType | Volume |
Automatic summarization,Computer science,Machine translation,Utterance,Speech recognition,Artificial intelligence,Natural language processing,Sentence | Conference | D09-1 |
Citations | PageRank | References |
1 | 0.42 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erin Fitzgerald | 1 | 125 | 8.71 |
| Frederick Jelinek | 2 | 139 | 23.22 |
| Keith B. Hall | 3 | 734 | 40.73 |