scholarly journals Corpus-Based Methods for Recognizing the Gender of Anthroponyms

Names ◽  
2021 ◽  
Vol 69 (3) ◽  
pp. 16-27
Author(s):  
Rogelio Nazar ◽  
Irene Renau ◽  
Nicolas Acosta ◽  
Hernan Robledo ◽  
Maha Soliman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Information Extraction ◽  
Machine Translation ◽  
Language Processing ◽  
Large Scale ◽  
Anaphora Resolution ◽  
Machine Learning Methods ◽  
Manual Processing ◽  
Large Corpus

This paper presents a series of methods for automatically determining the gender of proper names, based on their co-occurrence with words and grammatical features in a large corpus. Although the results obtained were for Spanish given names, the method presented here can be easily replicated and used for names in other languages. Most methods reported in the literature use pre-existing lists of first names that require costly manual processing and tend to become quickly outdated. Instead, we propose using corpora. Doing so offers the possibility of obtaining real and up-to-date name-gender links. To test the effectiveness of our method, we explored various machine-learning methods as well as another method based on simple frequency of co-occurrence. The latter produced the best results: 93% precision and 88% recall on a database of ca. 10,000 mixed names. Our method can be applied to a variety of natural language processing tasks such as information extraction, machine translation, anaphora resolution or large-scale delivery or email correspondence, among others.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

2021 ◽  
Author(s):  
Xinxu Shen ◽  
Troy Houser ◽  
David Victor Smith ◽  
Vishnu P. Murty
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Individual Difference ◽  
Language Processing ◽  
Large Scale ◽  
High Reliability ◽  
Difference Analysis ◽  
Universal Sentence ◽  
Natural Language Processing Tool

The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recall. We compared the reliability in scoring made between two independent raters (i.e., hand-scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique, video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand-scoring, and further that the results using USE outperformed another popular natural language processing tool, GloVe. In study two, we tested whether our automated approach remained valid when testing individual’s varying on clinically-relevant dimensions that influence episodic memory, age and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approaches implementing USE are a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.

scholarly journals Text Classification Algorithms: A Survey

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 150 ◽  
Author(s):  
Kowsari ◽  
Jafari Meimandi ◽  
Heidarysafa ◽  
Mendu ◽  
Barnes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Text Classification ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Machine Learning Methods ◽  
Linear Relationships ◽  
Reduction Methods ◽  
Complex Models

In recent years, there has been an exponential growth in the number of complex documentsand texts that require a deeper understanding of machine learning methods to be able to accuratelyclassify texts in many applications. Many machine learning approaches have achieved surpassingresults in natural language processing. The success of these learning algorithms relies on their capacityto understand complex models and non-linear relationships within data. However, finding suitablestructures, architectures, and techniques for text classification is a challenge for researchers. In thispaper, a brief overview of text classification algorithms is discussed. This overview covers differenttext feature extractions, dimensionality reduction methods, existing algorithms and techniques, andevaluations methods. Finally, the limitations of each technique and their application in real-worldproblems are discussed.

scholarly journals Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

2021 ◽  
Vol 15 ◽  
Author(s):  
Nora Hollenstein ◽  
Cedric Renggli ◽  
Benjamin Glaus ◽  
Maria Barrett ◽  
Marius Troendle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Brain Activity ◽  
Training Data ◽  
Human Cognition ◽  
Special Focus ◽  
Eeg Data

Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity for this purpose is largely unexplored as of yet. In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. We present a multi-modal machine learning architecture that learns jointly from textual input as well as from EEG features. We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. For more complex tasks such as relation detection, only the contextualized BERT embeddings outperform the baselines in our experiments, which raises the need for further research. Finally, EEG data shows to be particularly promising when limited training data is available.

scholarly journals Neural Language Modeling for Molecule Generation

2021 ◽  
Author(s):  
Sanjar Adilov
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Drug Design ◽  
Natural Language ◽  
Language Processing ◽  
De Novo ◽  
Language Modeling ◽  
Machine Learning Methods

Generative neural networks have shown promising results in <i>de novo</i> drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this paper, we present a survey of various machine learning methods for SMILES-based language modeling and propose our benchmarking results on a standardized subset of ChEMBL database.

scholarly journals Neural Language Modeling for Molecule Generation

2021 ◽  
Author(s):  
Sanjar Adilov
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Drug Design ◽  
Natural Language ◽  
Language Processing ◽  
De Novo ◽  
Language Modeling ◽  
Machine Learning Methods

Generative neural networks have shown promising results in <i>de novo</i> drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this paper, we present a survey of various machine learning methods for SMILES-based language modeling and propose our benchmarking results on a standardized subset of ChEMBL database.

scholarly journals APMorph: finite-state transducer for Amazigh pronominal morphology

2021 ◽  
Vol 11 (1) ◽  
pp. 699
Author(s):  
Rachid Ammari ◽  
Ahbib Zenkoua
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Automatic Processing ◽  
Finite State ◽  
Finite State Transducer ◽  
The Relationship

Our work aims to present an amazigh pronominal morphological analyzer (APMorph) based on xerox’s finite-state transducer (XFST). Our system revolves around a large lexicon named “APlex” including the affixed pronoun to the noun and to the verb and the characteristics relating to each lemma. A set of rules are added to define the inflectional behavior and morphosyntactic links of each entry as well as the relationship between the different lexical units. The implementation and the evaluation of our approach will be detailed within this article. The use of XFST remains a relevant choice in the sense that this platform allows both analysis and generation. The robustness of our system makes it able to be integrated in other applications of natural language processing (NLP) especially spellchecking, machine translation, and machine learning. This paper presents a continuation of our previous works on the automatic processing of Amazigh nouns and verbs.

Comparative Analysis of Language Translation and Detection System Using Machine Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 1200-1211
Author(s):  
Aishwarya R. Verma
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Short Term Memory ◽  
Detection System ◽  
English Text ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract: Words are the meaty component which can be expressed through speech, writing or signals. It is important that the actual message or meaning of the words sent must conveys the same meaning to the one receives. The evolution from manual language translator to the digital machine translation have helped us a lot for finding the exact meaning such that each word must give at least close to exact actual meaning. To make machine translator more human-friendly feeling, natural language processing (NLP) with machine learning (ML) can make the best combination. The main challenges in machine translated sentence can involve ambiguities, lexical divergence, syntactic, lexical mismatches, semantic issues, etc. which can be seen in grammar, spellings, punctuations, spaces, etc. After analysis on different algorithms, we have implemented a two different machine translator using two different Long Short-Term Memory (LSTM) approaches and performed the comparative study of the quality of the translated text based on their respective accuracy. We have used two different training approaches of encodingdecoding techniques using same datasets, which translates the source English text to the target Hindi text. To detect the text entered is English or Hindi language, we have used Sequential LSTM training model for which the analysis has been performed based on its accuracy. As the result, the first LSTM trained model is 84% accurate and the second LSTM trained model is 71% accurate in its translation from English to Hindi text, while the detection LSTM trained model is 78% accurate in detecting English text and 81% accurate in detecting Hindi text. This study has helped us to analyze the appropriate machine translation based on its accuracy. Keywords: Accuracy, Decoding, Machine Learning (ML), Detection System, Encoding, Long Short-Term Memory (LSTM), Machine Translation, Natural Language Processing (NLP), Sequential

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