Bidirectional LSTM-CRF Models for Sequence Tagging
Indexed inarxivdatacite
Abstract
At the moment, the vast majority of Portuguese archives with an online presence use a software solution to manage their finding aids: e.g. Digitarq or Archeevo. Most of these finding aids are written in natural language without any annotation that would enable a machine to identify named entities, geographical locations or even some dates. That would allow the machine to create smart browsing tools on top of those record contents like entity linking and record linking. In this work we have created a set of datasets to train Machine Learning algorithms to find those named entities and geographical locations. After training several algorithms we tested them in several datasets and registered their precision and…
Citation impact
3,284
total citations
- FWCI
- 237.28
- Percentile
- 100%
- References
- 23
Citations per year
Authors
3- ZHZhiheng HuangCorresponding
- XWXu, Wei
- KYKai Yu
Topics & keywords
Topics
Keywords
- Sequence (biology)
- Computer science
- Natural language processing
- Artificial intelligence
- Speech recognition
- Biology
- Genetics
UN Sustainable Development Goals
- Quality Education
No related works found for this paper.