Toward Universal Word Sense Disambiguation Using Deep Neural Networks

Calvo, Hiram; Rocha-Ramírez, Arturo P.; Moreno-Armendáriz, Marco A.; Duchanoy, Carlos A.

doi:10.1109/access.2019.2914921

Cited by 18 publications

(6 citation statements)

References 10 publications

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“…By doing a literature review, the challenge in each scheme can be assessed in terms of geographic facts, cost and control overheads, and time restrictions by assessing the literature. Based on the parameters above and the amount of energy consumed, the virtual infrastructure is built and packets are routed in response to queries [9] Razak (2012).…”

Section: Erman Et Al (2012)mentioning

confidence: 99%

Improved Network Validity Using Various Soft Computing Techniques

Yuvaraju¹,

Elakkiyavendan²

2023

Intelligent Automation &Amp; Soft Computing

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Nowadays, when a life span of sensor nodes are threatened by the shortage of energy available for communication, sink mobility is an excellent technique for increasing its lifespan. When communicating via a WSN, the use of nodes as a transmission method eliminates the need for a physical medium. Sink mobility in a dynamic network topology presents a problem for sensor nodes that have reserved resources. Unless the route is revised and changed to reflect the location of the mobile sink location, it will be inefficient for delivering data effectively. In the clustering strategy, nodes are grouped together to improve communication, and the cluster head receives data from compactable nodes. The sink receives the aggregated data from the head. The cluster head is the central node in the conventional technique. A single node uses more energy than a node that is routed to a dead node. Increasing the number of people using a route shortens its lifespan. The proposed work demonstrates the effectiveness with which sensor node paths can be modified at a lower cost by utilising the virtual grid. The best routes are maintained mostly by sink node communication on routes based on dynamic route adjustment (VGDRA). Only specific nodes are acquired to re-align data supply to the mobile sink in accordance with new paradigms of route reconstruction. According to the results, VGDRA schemes have a longer life span because of the reduced number of loops.

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Section: Erman Et Al (2012)mentioning

confidence: 99%

Improved Network Validity Using Various Soft Computing Techniques

Yuvaraju¹,

Elakkiyavendan²

2023

Intelligent Automation &Amp; Soft Computing

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“…Previous research pointed out that machine translation models suffer from issues related to polysemy and multiple word senses (Calvo et al, 2019;Huang et al, 2011). To tackle these, we experimented with embeddings which we trained on our own small domain of English translations, as well as different pretrained word embeddings.…”

Section: Training Mt Systems For Sumerianmentioning

confidence: 99%

Towards the First Machine Translation System for Sumerian Transliterations

Punia¹,

Schenk²,

Chiarcos³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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The Sumerian cuneiform script was invented more than 5,000 years ago and represents one of the oldest in history. We present the first attempt to translate Sumerian texts into English automatically. We publicly release high-quality corpora for standardized training and evaluation and report results on experiments with supervised, phrase-based, and transfer learning techniques for machine translation. Quantitative and qualitative evaluations indicate the usefulness of the translations. Our proposed methodology provides a broader audience of researchers with novel access to the data, accelerates the costly and time-consuming manual translation process, and helps them better explore the relationships between Sumerian cuneiform and Mesopotamian culture.

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“…Prior to apply ShotgunWSD2.0 they remove stopwords, applied Porter stemming algorithm on remaining words to eliminate most common morphological and in flexional endings [7]. Hiram Calvo, Arturo P. Rocha-Ramirez at.al(2019) Proposes a word senesce disambiguation model based on embedding representation of words using deep neural networks and obtained F1 Score 63.30.They used text processing tasks like convert text into lower case and applied Porter and Snowball stemming algorithms to remove suffixes [8].Axel Groß-Klußmann and at.al(2019) proposed Un-supervised and Supervised expert identification system to identify the major financial developments in economic regions and to predict profitable investments in stock market. They used Python NLTK to eliminate noise such as to removal of punctuations, stopwords, hashtags, casefolding, reduced the fraction of noise induced by informal language, applied Porter stemming algorithm on financial twitter datasets [10].…”

Section: Related Workmentioning

confidence: 99%

The Unprejudiced Stemmer to Prevent Etymological Behavior of Stemmed Morphemes Of Social Media Corpora

Rao*,

2019

IJITEE

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Sentiment Analysis is an application of Natural Langue Processing to analyze social media corpora to extract insights of corpora. Sentiment analytical results are the real feedback of the customers, which enables the organizations and companies to take appropriate decision on their products and business policies. Stemming plays in-evitable and vital role in sentiment analysis. Stemming is one of the phase of preprocessing the social media corpora. Today most of the researches uses strong stemmers to identify stem words of social media corpora. The most popular stemming algorithms such as Lancaster and Porter stemming algorithms causes prejudiced the meaning of the words. The over-stemmed words mislead the sentiment classification process. To prevent the over-stemming the Unprejudiced lighter stemming algorithm is proposed to sustain the meaning of the stemmed words. The propose Un-prejudiced algorithm uses lexical database and Parts of speech of Python Natural Language Tool Kit. There are a few stemming algorithm accuracy evaluation methods, in this paper we focused on Paice Error-rate relative to truncation (ERRT) measure to evaluate the accuracy of Lancaster, Porter and Unprejudiced stemming algorithms. The experiments were conducted on 25,758 source words and results were evaluated using Paice stem evaluation method and Sirsat method. The Paice Evaluation ERRT values 0.47209, 0.28703, 0.15502 of Lancaster, Porter, Unprejudiced respectively are proved that the Unprejudiced stemmer is more accurate than Lancaster and Porter. Sirsat’s stem evaluation method Average Words Conflation Factor (AWCF) results 10310.31, 14031.17, 23349.87 of Lancaster, Porter, Unprejudiced respectively are also proved the Unprejudiced stemming algorithm is more accurate than Lancaster and Porter stemming algorithms.

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Toward Universal Word Sense Disambiguation Using Deep Neural Networks

Cited by 18 publications

References 10 publications

Improved Network Validity Using Various Soft Computing Techniques

Improved Network Validity Using Various Soft Computing Techniques

Towards the First Machine Translation System for Sumerian Transliterations

The Unprejudiced Stemmer to Prevent Etymological Behavior of Stemmed Morphemes Of Social Media Corpora

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