Neural correlates of word representation vectors in natural language processing models: Evidence from representational similarity analysis of event‐related brain potentials

Natural Language ◽

Machine Translation ◽

Language Processing ◽

Vital Role ◽

Attention Mechanism ◽

Neural Machine Translation ◽

Translation Model ◽

Word representation plays a vital role in most Natural Language Processing systems, especially for Neural Machine Translation. It tends to capture semantic and similarity between individual words well, but struggle to represent the meaning of phrases or multi-word expressions. In this paper, we investigate a method to generate and use phrase information in a translation model. To generate phrase representations, a Primary Phrase Capsule network is first employed, then iteratively enhancing with a Slot Attention mechanism. Experiments on the IWSLT English to Vietnamese, French, and German datasets show that our proposed method consistently outperforms the baseline Transformer, and attains competitive results over the scaled Transformer with two times lower parameters.

Lecture Notes on Data Engineering and Communications Technologies - Innovative Systems for Intelligent Health Informatics ◽

An Approach Based Natural Language Processing for DNA Sequences Encoding Using the Global Vectors for Word Representation

10.1007/978-3-030-70713-2_53 ◽

2021 ◽

pp. 577-585

Author(s):

Brahim Matougui ◽

Hacene Belhadef ◽

Ilham Kitouni

Keyword(s):

Natural Language ◽

Language Processing ◽

Dna Sequences ◽

International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) ◽

Similarity Analysis of Patent Claims Using Natural Language Processing Techniques

10.1109/iccima.2007.40 ◽

2007 ◽

Cited By ~ 11

Author(s):

Kishore Vama Indukuri ◽

Anurag Anil Ambekar ◽

Ashish Sureka

Keyword(s):

Natural Language ◽

Language Processing ◽

Similarity Analysis ◽

Processing Techniques

Word Representation

The Oxford Handbook of Computational Linguistics 2nd edition ◽

10.1093/oxfordhb/9780199573691.013.57 ◽

2018 ◽

Author(s):

Omer Levy

Keyword(s):

Natural Language ◽

Vector Space ◽

Language Processing ◽

Computational Models ◽

Fundamental Challenge ◽

A fundamental challenge in natural-language processing is to represent words as mathematical entities that can be read, reasoned, and manipulated by computational models. The current leading approach represents words as vectors in a continuous real-valued space, in such a way that similarities in the vector space correlate with semantic similarities between words. This chapter surveys various frameworks and methods for acquiring word vectors, while tying together related ideas and concepts.

A Year of Papers Using Biomedical Texts:

Yearbook of Medical Informatics ◽

10.1055/s-0040-1701997 ◽

2020 ◽

Vol 29 (01) ◽

pp. 221-225

Author(s):

Cyril Grouin ◽

Natalia Grabar ◽

Keyword(s):

Natural Language ◽

Language Processing ◽

Research Teams ◽

Word Representation ◽

The Will ◽

Biomedical Texts ◽

Selection Of

Objectives: Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field. Methods: We performed an automatic and manual pre-selection of papers to be reviewed and finally selected the best NLP papers of the year. We also propose an analysis of the content of NLP publications in 2019. Results: Three best papers have been selected this year including the generation of synthetic record texts in Chinese, a method to identify contradictions in the literature, and the BioBERT word representation. Conclusions: The year 2019 was very rich and various NLP issues and topics were addressed by research teams. This shows the will and capacity of researchers to move towards robust and reproducible results. Researchers also prove to be creative in addressing original issues with relevant approaches.

2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) ◽

An Introduction of Deep Learning Based Word Representation Applied to Natural Language Processing

10.1109/mlbdbi48998.2019.00025 ◽

2019 ◽

Author(s):

Zihao Fu

Keyword(s):

Deep Learning ◽

Natural Language ◽

Language Processing ◽

Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics ◽

Representation of Words in Natural Language Processing: A Survey

10.17721/1812-5409.2019/2.10 ◽

2019 ◽

pp. 82-87

Author(s):

Y. Losieva

Keyword(s):

Neural Networks ◽

Natural Language ◽

Computational Linguistics ◽

Language Processing ◽

Word Embeddings ◽

Vector Representation ◽

Advantages And Disadvantages ◽

Word Representation ◽

The Relationship

The article is devoted to research to the state-of-art vector representation of words in natural language processing. Three main types of vector representation of a word are described, namely: static word embeddings, use of deep neural networks for word representation and dynamic) word embeddings based on the context of the text. This is a very actual and much-demanded area in natural language processing, computational linguistics and artificial intelligence at all. Proposed to consider several different models for vector representation of the word (or word embeddings), from the simplest (as a representation of text that describes the occurrence of words within a document or learning the relationship between a pair of words) to the multilayered neural networks and deep bidirectional transformers for language understanding, are described chronologically in relation to the appearance of models. Improvements regarding previous models are described, both the advantages and disadvantages of the presented models and in which cases or tasks it is better to use one or another model.

Natural Language Processing and Enhanced Clinical Decision Making Radiology and VINCI

PsycEXTRA Dataset ◽

10.1037/e615572012-015 ◽

2012 ◽

Author(s):

Eliot Siegel

Keyword(s):

Decision Making ◽

Natural Language ◽

Language Processing ◽

Clinical Decision Making ◽

Clinical Decision

Natural Language Processing in the Clinical Setting

PsycEXTRA Dataset ◽

10.1037/e615572012-013 ◽

2012 ◽

Author(s):

Thomas H. Payne

Keyword(s):

Natural Language ◽

Language Processing ◽

Clinical Setting

A Review and evaluation of Machine Translation methods for Lumasaaba

Journal of Digital Science ◽

10.33847/2686-8296.2.1_1 ◽

2020 ◽

pp. 3-17

Author(s):

Peter Nabende

Keyword(s):

Natural Language ◽

Machine Translation ◽

Language Processing ◽

Research Area ◽

Data Driven ◽

East African ◽

Data Set ◽

African Languages ◽

Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.