Genetic programming for natural language processing

Araujo, Lourdes

doi:10.1007/s10710-019-09361-5

Cited by 7 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, no references have been found about the use of genetic programming (GP) [73] in the field of semantic address matching. GP has several advantages over other machine learning methods, including the ability to provide results that can be easily interpreted, based on programs, rules, or functions, as well as the ability to easily incorporate specific knowledge about a problem, despite its efficiency issues, which are primarily due to a time-consuming fitness function computation [74]. In Figure 10, the main research gaps are illustrated.…”

Section: Research Gapsmentioning

confidence: 99%

Automatic Identification of Addresses: A Systematic Literature Review

Cruz

Vanneschi

Paìnho

et al. 2021

IJGI

View full text Add to dashboard Cite

Address matching continues to play a central role at various levels, through geocoding and data integration from different sources, with a view to promote activities such as urban planning, location-based services, and the construction of databases like those used in census operations. However, the task of address matching continues to face several challenges, such as non-standard or incomplete address records or addresses written in more complex languages. In order to better understand how current limitations can be overcome, this paper conducted a systematic literature review focused on automated approaches to address matching and their evolution across time. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, resulting in a final set of 41 papers published between 2002 and 2021, the great majority of which are after 2017, with Chinese authors leading the way. The main findings revealed a consistent move from more traditional approaches to deep learning methods based on semantics, encoder-decoder architectures, and attention mechanisms, as well as the very recent adoption of hybrid approaches making an increased use of spatial constraints and entities. The adoption of evolutionary-based approaches and privacy preserving methods stand as some of the research gaps to address in future studies.

show abstract

Section: Research Gapsmentioning

confidence: 99%

Automatic Identification of Addresses: A Systematic Literature Review

Cruz

Vanneschi

Paìnho

et al. 2021

IJGI

View full text Add to dashboard Cite

show abstract

“…The first applications of GP in the field of NLP were mostly related to the identification of the semantic structure of the language. The main reason relies on the fact that the syntax-tree representation used in GP is similar to the one commonly employed to describe the syntax of a language [5]. Thus, it comes naturally to use GP for the task of identifying the syntactic structures of the language.…”

Section: Related Workmentioning

confidence: 99%

“…Despite the existence of different techniques dedicated to the next word prediction task, the use of evolutionary algorithms was not fully explored. In particular, Genetic Programming (GP) was applied to different tasks in the context of NLP [5], but no specific effort was dedicated to this prediction task.…”

Section: Introductionmentioning

confidence: 99%

Towards an evolutionary-based approach for natural language processing

Manzoni

Jakobović

Mariot

et al. 2020

Proceedings of the 2020 Genetic and Evolutionary Computation Conference

View full text Add to dashboard Cite

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. CCS CONCEPTS • Computing methodologies → Natural language processing; Genetic programming; Bio-inspired approaches.

show abstract

“…The complex texture features of SAR images and the similarity between targets bring great challenges to object detection and recognition [8]. Recently, Convolutional Neural Networks (CNN) have been applied to computer vision, such as image detection [9], semantic classification, and other tasks [10].…”

Section: Introductionmentioning

confidence: 99%

CCDS-YOLO: Multi-Category Synthetic Aperture Radar Image Object Detection Model Based on YOLOv5s

Huang,

Liu,

Liu

et al. 2023

Electronics

View full text Add to dashboard Cite

Synthetic Aperture Radar (SAR) is an active microwave sensor that has attracted widespread attention due to its ability to observe the ground around the clock. Research on multi-scale and multi-category target detection methods holds great significance in the fields of maritime resource management and wartime reconnaissance. However, complex scenes often influence SAR object detection, and the diversity of target scales also brings challenges to research. This paper proposes a multi-category SAR image object detection model, CCDS-YOLO, based on YOLOv5s, to address these issues. Embedding the Convolutional Block Attention Module (CBAM) in the feature extraction part of the backbone network enables the model’s ability to extract and fuse spatial information and channel information. The 1 × 1 convolution in the feature pyramid network and the first layer convolution of the detection head are replaced with the expanded convolution, Coordinate Conventional (CoordConv), forming a CRD-FPN module. This module more accurately perceives the spatial details of the feature map, enhancing the model’s ability to handle regression tasks compared to traditional convolution. In the detector segment, a decoupled head is utilized for feature extraction, offering optimal and effective feature information for the classification and regression branches separately. The traditional Non-Maximum Suppression (NMS) is substituted with the Soft Non-Maximum Suppression (Soft-NMS), successfully reducing the model’s duplicate detection rate for compact objects. Based on the experimental findings, the approach presented in this paper demonstrates excellent results in multi-category target recognition for SAR images. Empirical comparisons are conducted on the filtered MSAR dataset. Compared with YOLOv5s, the performance of CCDS-YOLO has been significantly improved. The mAP@0.5 value increases by 3.3% to 92.3%, the precision increases by 3.4%, and the mAP@0.5:0.95 increases by 6.7%. Furthermore, in comparison with other mainstream detection models, CCDS-YOLO stands out in overall performance and anti-interference ability.

show abstract

Genetic programming for natural language processing

Cited by 7 publications

References 47 publications

Automatic Identification of Addresses: A Systematic Literature Review

Automatic Identification of Addresses: A Systematic Literature Review

Towards an evolutionary-based approach for natural language processing

CCDS-YOLO: Multi-Category Synthetic Aperture Radar Image Object Detection Model Based on YOLOv5s

Contact Info

Product

Resources

About