| 1 |
Delexicalized Word Embeddings for Cross-lingual Dependency Parsing
|
|
|
|
In: EACL ; https://hal.inria.fr/hal-01590639 ; EACL, Apr 2017, Valencia, Spain. pp.241 - 250, ⟨10.18653/v1/E17-1023⟩ ; http://eacl2017.org/ (2017)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
IRISA at DeFT2017 : classification systems of increasing complexity ; Participation de l'IRISA à DeFT2017 : systèmes de classification de complexité croissante
|
|
|
|
In: DeFT 2017 - Défi Fouille de texte ; https://hal.archives-ouvertes.fr/hal-01643993 ; DeFT 2017 - Défi Fouille de texte, Jun 2017, Orléans, France. pp.1-10 (2017)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Invariance: a Theoretical Approach for Coding Sets of Words Modulo Literal (Anti)Morphisms
|
|
|
|
In: Springer, LNCS. ; https://hal-normandie-univ.archives-ouvertes.fr/hal-02117030 ; Springer, LNCS., 2017, pp.214-227 (2017)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Things and Strings and More: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence, Topic Modeling, and Word Embedding
|
|
Ju, Yiting. - : eScholarship, University of California, 2017
|
|
In: Ju, Yiting. (2017). Things and Strings and More: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence, Topic Modeling, and Word Embedding. 0035: Geography. Retrieved from: http://www.escholarship.org/uc/item/4w60s702 (2017)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
An empirical study of the Algerian dialect of Social network
|
|
|
|
In: ICNLSSP 2017 - International Conference on Natural Language, Signal and Speech Processing ; https://hal.inria.fr/hal-01659997 ; ICNLSSP 2017 - International Conference on Natural Language, Signal and Speech Processing, Dec 2017, Casablanca, Morocco ; http://icnlssp.isga.ma (2017)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Cognitive Intelligence in Relational Databases ...
|
|
|
|
Abstract:
We evaluate the applicability of distributed language embedding techniques from the domain of natural language processing to relational data. Relational data is typically stored in SQL databases. We apply modern distributed representations of words (Tomas Mikolov 2013c) and paragraph (Quoc V. Le 2014) techniques to this structured data and attempt to unlock the potential of enhanced cognitive querying. The research intention is to be able to perform queries which are non-trivial to perform using the SQL dialect alone. We tokenize the IMDB 5000 movie data-set to generate embeddings using word2vec and a modified version of doc2vec that we term as row2vec. We discuss the effects of various hyperparameter choices and tokenization techniques. We visualise these embedding using PCA and present the results for certain queries. Keywords: Word embedding, databases, word2vec, cognitive querying. ...
|
|
Keyword:
cognitive querying; databases; Word embedding; word2vec
|
|
URL: https://dx.doi.org/10.13016/m2wzew-tsoz
http://mdsoar.org/handle/11603/15494
|
|
BASE
|
|
Hide details
|
|
|
| 7 |
Linguistic Knowledge Transfer for Enriching Vector Representations
|
|
|
|
In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1500571436042414 (2017)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Induction de lexiques bilingues à partir de corpus comparables et parallèles
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
