- author = "Luca Manzoni and Domagoj Jakobovic and Luca Mariot and Stjepan Picek and Mauro Castelli",
- title = "Towards an Evolutionary-Based Approach for Natural Language Processing",
- year = "2020",
- editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann",
- isbn13 = "9781450371285",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
-
URL = "
https://doi.org/10.1145/3377930.3390248",
-
DOI = "
doi:10.1145/3377930.3390248",
- booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference",
- pages = "985--993",
- size = "9 pages",
- keywords = "genetic algorithms, genetic programming, NLP, natural language processing, next word prediction",
- address = "internet",
- series = "GECCO '20",
- month = jul # " 8-12",
- organisation = "SIGEVO",
- abstract = "Tasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community. The increased interest in this area is mainly due to the success of deep learning methods. Genetic Programming (GP), however, was not under the spotlight with respect to NLP tasks. Here, we propose a first proof-of-concept that combines GP with the well established NLP tool word2vec for the next word prediction task. The main idea is that, once words have been moved into a vector space, traditional GP operators can successfully work on vectors, thus producing meaningful words as the output. To assess the suitability of this approach, we perform an experimental evaluation on a set of existing newspaper headlines. Individuals resulting from this (pre-)training phase can be employed as the initial population in other NLP tasks, like sentence generation, which will be the focus of future investigations, possibly employing adversarial co-evolutionary approaches.",
-
notes = "next word prediction. Word2Vec (cosine) vectors as
inputs (leafs) for GP tree. GP tree returns vector,
look it up, finess = mean cosine. Million News headline
(newspapers 2003--2017) > 6 words in sentence, 267292
examples, train on ~2700. Dimensionality of vec2word
embedding 10..100. Best of 30 GP runs.
Also known as \cite{10.1145/3377930.3390248} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",
