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Deep Learning Methods for Natural Language Processing
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Deep Learning Methods for Natural Language Processing

A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990).

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 8099

Special Issue Editor

Dr. Nicholas Ampazis

E-Mail Website
Guest Editor
Department of Financial and Management Engineering, University of the Aegean, Kountouriotou 41, 82100 Chios, Greece
Interests: neural networks; machine learning; data mining; recommender systems

Special Issue Information

Dear Colleagues,

In recent years, Deep Learning approaches have shown great success in the field of Natural Language Processing (NLP). Recent research has demonstrated that Deep Learning based methods have achieved state-of-the-art performances on many NLP tasks including, among others, sentiment analysis, text classification, text generation, question answering, and automatic machine translation. However, many of the problems in NLP are not yet fully addressed by existing Deep Learning models and there is a need to develop new methods and models that can be used to improve the efficiency and quality of NLP systems.

The aim of this Special Issue is to invite researchers to present recent advances in the application of Deep Learning approaches to Natural Language Processing and to provide an opportunity to discuss future directions in this exciting field.

The topics of interest for this Special Issue include, but are not limited to:

* Text classification

* Sentiment analysis

* Language modeling

* Text generation

* Question answering

* Text Summarization

* Information retrieval

* Text Segmentation and Clustering

* Machine translation

* Word embedding

Additionally of interest are papers that develop new Deep Learning models for NLP tasks or develop new methods for improving the efficiency and accuracy of existing Deep Learning models for NLP.

Dr. Nicholas Ampazis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • machine learning
  • natural language processing
  • text analytics

Published Papers (3 papers)

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Research

13 pages, 1946 KiB  
Article
A Multi-Input Machine Learning Approach to Classifying Sex Trafficking from Online Escort Advertisements
by Lucia Summers, Alyssa N. Shallenberger, John Cruz and Lawrence V. Fulton
Mach. Learn. Knowl. Extr. 2023, 5(2), 460-472; https://doi.org/10.3390/make5020028 - 10 May 2023
Viewed by 2423
Abstract
Sex trafficking victims are often advertised through online escort sites. These ads can be publicly accessed, but law enforcement lacks the resources to comb through hundreds of ads to identify those that may feature sex-trafficked individuals. The purpose of this study was to [...] Read more.
Sex trafficking victims are often advertised through online escort sites. These ads can be publicly accessed, but law enforcement lacks the resources to comb through hundreds of ads to identify those that may feature sex-trafficked individuals. The purpose of this study was to implement and test multi-input, deep learning (DL) binary classification models to predict the probability of an online escort ad being associated with sex trafficking (ST) activity and aid in the detection and investigation of ST. Data from 12,350 scraped and classified ads were split into training and test sets (80% and 20%, respectively). Multi-input models that included recurrent neural networks (RNN) for text classification, convolutional neural networks (CNN, specifically EfficientNetB6 or ENET) for image/emoji classification, and neural networks (NN) for feature classification were trained and used to classify the 20% test set. The best-performing DL model included text and imagery inputs, resulting in an accuracy of 0.82 and an F1 score of 0.70. More importantly, the best classifier (RNN + ENET) correctly identified 14 of 14 sites that had classification probability estimates of 0.845 or greater (1.0 precision); precision was 96% for the multi-input model (NN + RNN + ENET) when only the ads associated with the highest positive classification probabilities (>0.90) were considered (n = 202 ads). The models developed could be productionalized and piloted with criminal investigators, as they could potentially increase their efficiency in identifying potential ST victims. Full article
(This article belongs to the Special Issue Deep Learning Methods for Natural Language Processing)
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16 pages, 794 KiB  
Article
Detection of Temporal Shifts in Semantics Using Local Graph Clustering
by Neil Hwang, Shirshendu Chatterjee, Yanming Di and Sharmodeep Bhattacharyya
Mach. Learn. Knowl. Extr. 2023, 5(1), 128-143; https://doi.org/10.3390/make5010008 - 13 Jan 2023
Cited by 1 | Viewed by 2051
Abstract
Many changes in our digital corpus have been brought about by the interplay between rapid advances in digital communication and the current environment characterized by pandemics, political polarization, and social unrest. One such change is the pace with which new words enter the [...] Read more.
Many changes in our digital corpus have been brought about by the interplay between rapid advances in digital communication and the current environment characterized by pandemics, political polarization, and social unrest. One such change is the pace with which new words enter the mass vocabulary and the frequency at which meanings, perceptions, and interpretations of existing expressions change. The current state-of-the-art algorithms do not allow for an intuitive and rigorous detection of these changes in word meanings over time. We propose a dynamic graph-theoretic approach to inferring the semantics of words and phrases (“terms”) and detecting temporal shifts. Our approach represents each term as a stochastic time-evolving set of contextual words and is a count-based distributional semantic model in nature. We use local clustering techniques to assess the structural changes in a given word’s contextual words. We demonstrate the efficacy of our method by investigating the changes in the semantics of the phrase “Chinavirus”. We conclude that the term took on a much more pejorative meaning when the White House used the term in the second half of March 2020, although the effect appears to have been temporary. We make both the dataset and the code used to generate this paper’s results available. Full article
(This article belongs to the Special Issue Deep Learning Methods for Natural Language Processing)
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19 pages, 2157 KiB  
Article
Learning Sentence-Level Representations with Predictive Coding
by Vladimir Araujo, Marie-Francine Moens and Alvaro Soto
Mach. Learn. Knowl. Extr. 2023, 5(1), 59-77; https://doi.org/10.3390/make5010005 - 09 Jan 2023
Viewed by 2497
Abstract
Learning sentence representations is an essential and challenging topic in the deep learning and natural language processing communities. Recent methods pre-train big models on a massive text corpus, focusing mainly on learning the representation of contextualized words. As a result, these models cannot [...] Read more.
Learning sentence representations is an essential and challenging topic in the deep learning and natural language processing communities. Recent methods pre-train big models on a massive text corpus, focusing mainly on learning the representation of contextualized words. As a result, these models cannot generate informative sentence embeddings since they do not explicitly exploit the structure and discourse relationships existing in contiguous sentences. Drawing inspiration from human language processing, this work explores how to improve sentence-level representations of pre-trained models by borrowing ideas from predictive coding theory. Specifically, we extend BERT-style models with bottom-up and top-down computation to predict future sentences in latent space at each intermediate layer in the networks. We conduct extensive experimentation with various benchmarks for the English and Spanish languages, designed to assess sentence- and discourse-level representations and pragmatics-focused assessments. Our results show that our approach improves sentence representations consistently for both languages. Furthermore, the experiments also indicate that our models capture discourse and pragmatics knowledge. In addition, to validate the proposed method, we carried out an ablation study and a qualitative study with which we verified that the predictive mechanism helps to improve the quality of the representations. Full article
(This article belongs to the Special Issue Deep Learning Methods for Natural Language Processing)
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