TY - GEN
T1 - Lexical Function Identification Using Word Embeddings and Deep Learning
AU - Hernández-Miranda, Arturo
AU - Gelbukh, Alexander
AU - Kolesnikova, Olga
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In this work, we report the results of our experiments on the task of distinguishing the semantics of verb-noun collocations in a Spanish corpus. This semantics was represented by four lexical functions of the Meaning-Text Theory. Each lexical function specifies a certain universal semantic concept found in any natural language. Knowledge of collocation and its semantic content is important for natural language processing, as collocation comprises the restrictions on how words can be used together. We experimented with a combination of GloVe word embeddings as a recent and extended algorithm for vector representation of words and a deep neural architecture, in order to recover most of the context of verb-noun collocations in a meaningful way which could discriminate among lexical functions. Our corpus was a collection of 1,131 Excelsior newspaper issues. As our results showed, the proposed deep neural architecture outperformed state-of-the-art supervised learning methods.
AB - In this work, we report the results of our experiments on the task of distinguishing the semantics of verb-noun collocations in a Spanish corpus. This semantics was represented by four lexical functions of the Meaning-Text Theory. Each lexical function specifies a certain universal semantic concept found in any natural language. Knowledge of collocation and its semantic content is important for natural language processing, as collocation comprises the restrictions on how words can be used together. We experimented with a combination of GloVe word embeddings as a recent and extended algorithm for vector representation of words and a deep neural architecture, in order to recover most of the context of verb-noun collocations in a meaningful way which could discriminate among lexical functions. Our corpus was a collection of 1,131 Excelsior newspaper issues. As our results showed, the proposed deep neural architecture outperformed state-of-the-art supervised learning methods.
KW - Deep learning
KW - Lexical function
KW - Meaning-Text Theory
KW - Word embeddings
UR - http://www.scopus.com/inward/record.url?scp=85075680543&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-33749-0_7
DO - 10.1007/978-3-030-33749-0_7
M3 - Contribución a la conferencia
AN - SCOPUS:85075680543
SN - 9783030337483
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 77
EP - 86
BT - Advances in Soft Computing - 18th Mexican International Conference on Artificial Intelligence, MICAI 2019, Proceedings
A2 - Martínez-Villaseñor, Lourdes
A2 - Batyrshin, Ildar
A2 - Marín-Hernández, Antonio
PB - Springer
T2 - 18th Mexican International Conference on Artificial Intelligence, MICAI 2019
Y2 - 27 October 2019 through 2 November 2019
ER -