Name
Abstract | ||
|---|---|---|
Natural language processing tools are used to automatically detect disturbances in transcribed speech of schizophrenia inpatients who speak Hebrew. We measure topic mutation over time and show that controls maintain more cohesive speech than inpatients. We also examine differences in how inpatients and controls use adjectives and adverbs to describe content words and show that the ones used by controls are more common than the those of inpatients. We provide experimental results and show their potential for automatically detecting schizophrenia in patients by means only of their speech patterns. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.18653/v1/w19-3010 | arXiv: Computation and Language |
DocType | Volume | ISSN |
Journal | abs/1904.07953 | CLPsych at NAACL 2019 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kfir Bar | 1 | 33 | 5.14 |
| Vered Zilberstein | 2 | 0 | 0.34 |
| Ido Ziv | 3 | 0 | 0.34 |
| Heli Baram | 4 | 0 | 0.34 |
| Nachum Dershowitz | 5 | 2818 | 473.00 |
| Samuel Itzikowitz | 6 | 13 | 2.50 |
| Eiran Vadim Harel | 7 | 0 | 0.34 |