Nikolaos Malandrakis, Anil Ramakrishna, Victor Martinez, Tanner Sorensen, Dogan Can, and Shrikanth Narayanan. The elisa situation frame extraction for low resource languages pipeline for lorehlt’2016. Machine Translation, 32(1-2):127–142, Springer, 2018.

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Abstract

This paper describes the Situation Frame extraction pipeline developed by team ELISA as a part of the DARPA Low Resource Languages for Emergent Incidents program. Situation Frames are structures describing humanitarian needs, including the type of need and the location affected by it. Situation Frames need to be extracted from text or speech audio in a low resource scenario where little data, including no annotated data, are available for the target language. Our Situation Frame pipeline is the final step of the overall ELISA processing pipeline and accepts as inputs the outputs of the ELISA machine translation and named entity recognition components. The inputs are processed by a combination of neural networks to detect the types of needs mentioned in each document and a second post-processing step connects needs to locations. The resulting Situation Frame system was used during the first yearly evaluation on extracting Situation Frames from text, producing encouraging results and was later successfully adapted to the speech audio version of the same task.

BibTeX Entry

@article{malandrakis2018elisa,
 abstract = {This paper describes the Situation Frame extraction pipeline developed by team ELISA as a part of the DARPA Low Resource Languages for Emergent Incidents program. Situation Frames are structures describing humanitarian needs, including the type of need and the location affected by it. Situation Frames need to be extracted from text or speech audio in a low resource scenario where little data, including no annotated data, are available for the target language. Our Situation Frame pipeline is the final step of the overall ELISA processing pipeline and accepts as inputs the outputs of the ELISA machine translation and named entity recognition components. The inputs are processed by a combination of neural networks to detect the types of needs mentioned in each document and a second post-processing step connects needs to locations. The resulting Situation Frame system was used during the first yearly evaluation on extracting Situation Frames from text, producing encouraging results and was later successfully adapted to the speech audio version of the same task.},
 author = {Malandrakis, Nikolaos and Ramakrishna, Anil and Martinez, Victor and Sorensen, Tanner and Can, Dogan and Narayanan, Shrikanth},
 doi = {10.1007/s10590-017-9204-4},
 issn = {0922-6567},
 journal = {Machine Translation},
 keywords = {Topic classification, Text classification, Situation Frames},
 link = {http://sail.usc.edu/publications/files/Malandrakis-MT2018.pdf},
 number = {1-2},
 pages = {127--142},
 publisher = {Springer},
 title = {The elisa situation frame extraction for low resource languages pipeline for lorehlt’2016},
 url = {https://doi.org/10.1007/s10590-017-9204-4},
 volume = {32},
 year = {2018}
}

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