| dc.contributor.author |
Sharma, Yashvardhan |
|
| dc.date.accessioned |
2023-01-02T10:45:18Z |
|
| dc.date.available |
2023-01-02T10:45:18Z |
|
| dc.date.issued |
2017-12 |
|
| dc.identifier.uri |
https://ceur-ws.org/Vol-2036/T3-3.pdf |
|
| dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8223 |
|
| dc.description.abstract |
Legal texts usually have a complex structure and reading through them is
a time-consuming and strenuous task. Hence it is essential to provide the
legal practitioners a concise representation of the text. Catchphrases are
those phrases which state the important issues present in the text, thus
effectively characterizing it. This paper proposes an approach for the
subtask 1 of the task IRLed (Information Retrieval from Legal
Documents), FIRE 2017. The proposed algorithm uses a three step
approach for extracting catchphrases from legal documents. |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
CEUR |
en_US |
| dc.subject |
Computer Science |
en_US |
| dc.subject |
Keyword Extraction |
en_US |
| dc.subject |
Legal Documents |
en_US |
| dc.subject |
Deep Learning |
en_US |
| dc.subject |
LSTM |
en_US |
| dc.subject |
Natural Language Processing |
en_US |
| dc.title |
Catchphrase Extraction from Legal Documents Using LSTM Networks |
en_US |
| dc.type |
Article |
en_US |