@inproceedings{conf/esws/ElSaharDGGL17,
  added-at = {2021-07-20T00:00:00.000+0200},
  author = {ElSahar, Hady and Demidova, Elena and Gottschalk, Simon and Gravier, Christophe and Laforest, Frédérique},
  biburl = {https://www.bibsonomy.org/bibtex/213efe26975743223c3445e896aae51be/dblp},
  booktitle = {ESWC (Satellite Events)},
  crossref = {conf/esws/2017s},
  editor = {Blomqvist, Eva and Hose, Katja and Paulheim, Heiko and Lawrynowicz, Agnieszka and Ciravegna, Fabio and Hartig, Olaf},
  ee = {https://doi.org/10.1007/978-3-319-70407-4_3},
  interhash = {84347f4d1dbaa3cbb4649be7c8355922},
  intrahash = {13efe26975743223c3445e896aae51be},
  isbn = {978-3-319-70407-4},
  keywords = {dblp},
  pages = {12-16},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  timestamp = {2024-04-10T03:13:03.000+0200},
  title = {Unsupervised Open Relation Extraction.},
  url = {http://dblp.uni-trier.de/db/conf/esws/eswc2017s.html#ElSaharDGGL17},
  volume = 10577,
  year = 2017
}

@inproceedings{elsahar2017unsupervised,
  abstract = {We explore methods to extract relations between named entities from free text in an unsupervised setting. In addition to standard feature extraction, we develop a novel method to re-weight word embeddings. We alleviate the problem of features sparsity using an individual feature reduction. Our approach exhibits a significant improvement by 5.8% over the state-of-the-art relation clustering scoring a F1-score of 0.416 on the NYT-FB dataset.},
  added-at = {2018-01-24T13:27:14.000+0100},
  author = {Elsahar, Hady and Demidova, Elena and Gottschalk, Simon and Gravier, Christophe and Laforest, Frederique},
  biburl = {https://www.bibsonomy.org/bibtex/20af38519561d89a93971f8ecb8938c7f/alexandriaproj},
  booktitle = {Proceedings of the ESWC 2017 Satellite Events},
  interhash = {84347f4d1dbaa3cbb4649be7c8355922},
  intrahash = {0af38519561d89a93971f8ecb8938c7f},
  keywords = {2017 alexandria},
  publisher = {Springer},
  timestamp = {2018-01-24T13:27:14.000+0100},
  title = {Unsupervised Open Relation Extraction},
  volume = {Lecture Notes in  Computer Science  (LNCS), vol 10577.},
  year = 2017
}

@inproceedings{elsahar2017unsupervised,
  abstract = {We explore methods to extract relations between named entities from free text in an unsupervised setting. In addition to standard feature extraction, we develop a novel method to re-weight word embeddings. We alleviate the problem of features sparsity using an individual feature reduction. Our approach exhibits a significant improvement by 5.8% over the state-of-the-art relation clustering scoring a F1-score of 0.416 on the NYT-FB dataset.},
  added-at = {2017-07-13T09:36:55.000+0200},
  author = {Elsahar, Hady and Demidova, Elena and Gottschalk, Simon and Gravier, Christophe and Laforest, Frederique},
  biburl = {https://www.bibsonomy.org/bibtex/25bac4cf1b2a36c360e3ea61efa60917c/sgottschalk},
  booktitle = {Proceedings of the ESWC 2017 Satellite Events},
  interhash = {84347f4d1dbaa3cbb4649be7c8355922},
  intrahash = {5bac4cf1b2a36c360e3ea61efa60917c},
  keywords = {myown},
  publisher = {Springer},
  timestamp = {2018-04-19T14:23:50.000+0200},
  title = {Unsupervised Open Relation Extraction},
  url = {https://link.springer.com/chapter/10.1007/978-3-319-70407-4_3},
  year = 2017
}

@inproceedings{elsahar2017unsupervised,
  abstract = {We explore methods to extract relations between named entities from free text in an unsupervised setting. In addition to standard feature extraction, we develop a novel method to re-weight word embeddings. We alleviate the problem of features sparsity using an individual feature reduction. Our approach exhibits a significant improvement by 5.8% over the state-of-the-art relation clustering scoring a F1-score of 0.416 on the NYT-FB dataset.},
  added-at = {2017-05-21T10:23:19.000+0200},
  author = {Elsahar, Hady and Demidova, Elena and Gottschalk, Simon and Gravier, Christophe and Laforest, Frederique},
  biburl = {https://www.bibsonomy.org/bibtex/20af38519561d89a93971f8ecb8938c7f/demidova},
  booktitle = {Proceedings of the ESWC 2017 Satellite Events},
  doi = {10.1007/978-3-319-70407-4_3},
  interhash = {84347f4d1dbaa3cbb4649be7c8355922},
  intrahash = {0af38519561d89a93971f8ecb8938c7f},
  keywords = {alexandria data4urbanmobility gottschalk myown wdaqua},
  publisher = {Springer},
  timestamp = {2018-10-23T09:52:15.000+0200},
  title = {Unsupervised Open Relation Extraction},
  volume = {Lecture Notes in  Computer Science  (LNCS), vol 10577.},
  year = 2017
}