Corpus ID: 6940060

Evolutionary Algorithms in Natural Language Processing

  title={Evolutionary Algorithms in Natural Language Processing},
  author={L. Bungum and Bj{\"o}rn Gamb{\"a}ck},
Natural language processing grapples with an ever-changing and moving target. The focus of study, natural language, is natural because it changes, interacts and evolves in various directions. The bio-inspired computational methods described as evolutionary computation create computational models that evolve a population of individuals to find a solution to a given problem. This paper investigates how evolutionary computation has been employed in natural language processing, ranging from efforts… Expand
Co-evolving Language and Social Structure Using a Genetic Algorithm
It is interesting how we can take a train of thought and transfer this into an other person's mind by pushing the air around us. Human language, this complex medium that distinctly separates humansExpand
Applications of Genetic Algorithm in Software Engineering , Distributed Computing and Machine Learning
There are different types of computational approaches like deterministic, random and evolutionary. Evolutionary techniques are also known as nature inspired techniques as these types of techniquesExpand
Applications of Genetic Algorithm in Software Engineering , Distributed Computing and Machine Learning
There are different types of computational approaches like deterministic, random and evolutionary. Evolutionary techniques are also known as nature inspired techniques as these types of techniquesExpand
Automatic Extension of Semantic Lexicons with a Bootstrapping Algorithm: Using Corpora to Learn Semantic Features
This book investigates and extends a bootstrapping approach which permits to extend high quality lexical resources with the help of very large corpora. Expand
Hybrid model to improve time complexity of words search in POS Tagging
A model which deals with the limitations of previously existing POS Tagging algorithm, namely, Memory Based Learning Algorithm and Multi-Domain Web Based Algorithm is presented and the time complexity of the model is calculated and a generalized formula for efficiency and performance is devised. Expand
Multi-qubit controlled NOT gates for artificial intelligence natural languages processing
A photonic miltiqubit controlled NOT gate based on a multi-wave mixing process in a cavity is proposed and Parameters matching condition is found that must be fulfilled for successful gate operation. Expand
Evolutionary Algorithm Applications in Data Mining
This paper is to present maximum number of applications of EA in Data mining field to present a consolidated view to the interested researchers in this aforesaid field. Expand
Combining graph connectivity and genetic clustering to improve biomedical summarization
This work aims to improve a summarization graph-based process combining genetic clustering with graph connectivity information, to demonstrate the appropriateness of using this combination of techniques for automatic summarization. Expand
Genetic graph-based in clustering applied to static and streaming data analysis
Tesis inedita leida en la Universidad Autonoma de Madrid, Escuela Politecnica Superior, Departamento de Ingenieria Informatica. Fecha de lectura: diciembre de 2014


Evolutionary Computing as a Tool for Grammar Development
An agent-based evolutionary computing technique is introduced, that is geared towards the automatic induction and optimization of grammars for natural language (grael) and can be used as an unsupervised grammar induction technique. Expand
Learning language using genetic algorithms
Strict pattern-based methods of grammar induction are often frustrated by the apparently inexhaustible variety of novel word combinations in large corpora. Statistical methods offer a possibleExpand
Statistical language learning
Eugene Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages and is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning. Expand
This paper proposes a new approach based on evolutionary hybrid algorithms to translate sentences in a specific technical context that has been enhanced by adaptive parameter control and validates the superior performance of the method in contrast to a statistical greedy decoder. Expand
Computational Grammar Induction for Linguists
This article will focus on first language acquisition with some suggestions forextensionstootherareas, and there are various domains in which learnability of a language might be inter-esting for linguists: e.g., second language acquisition, or the automatic extraction of grammars from corpora. Expand
Phylogenetic Grammar Induction
Joint induction in the multilingual model substantially outperforms independent learning, with larger gains both from more articulated phylogenies and as well as from increasing numbers of languages. Expand
Finding Cognate Groups Using Phylogenies
A generative phylogenetic model for automatically inducing cognate group structure from unaligned word lists that represents the process of transformation and transmission from ancestor word to daughter word, as well as the alignment between the words lists of the observed languages. Expand
Multi-objective optimisation of real-valued parameters of a hybrid MT system using Genetic Algorithms
An automated method is proposed for optimising the real-valued parameters of a hybrid Machine Translation system that employs pattern recognition techniques together with extensive monolingual corpora in the target language from which statistical information is extracted, and the results indicate the effectiveness of this approach. Expand
On the application of different evolutionary algorithms to the alignment problem in statistical machine translation
This paper proposes several evolutionary algorithms for computing alignments between two sentences in a parallel corpus and shows that the EDA-based algorithm outperforms the best participant systems in the two shared tasks proposed in the HLT-NAACL 2003 and the ACL 2005. Expand
Head-Driven Statistical Models for Natural Language Parsing
  • M. Collins
  • Computer Science
  • Computational Linguistics
  • 2003
Three statistical models for natural language parsing are described, leading to approaches in which a parse tree is represented as the sequence of decisions corresponding to a head-centered, top-down derivation of the tree. Expand