A Named Entity Recognition System for Malayalam using Neural Networks

Ajees, A P; Idicula, Sumam Mary

doi:10.1016/j.procs.2018.10.338

Cited by 9 publications

(4 citation statements)

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“…Ajees A, et. al discussed a NER (Named Entity Recognition) model to low resourced language Malayalam using Neural Networks [17]. Hovy et al, classified the concept of text summarization as extractive and abstractive model [18].…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Travel Recommender System by Mining Behavioral Attributes From Online Travelogues in Malayalam – A Low Resourced Language

2023

ecb

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Language technology involves various language processing tools and techniques which significantly contribute to Natural Language Processing (NLP). Among NLP, natural language text and speech processing are two emerging segments that require huge attention from research. Regional language processing with the advent of Artificial Intelligence brings umpteen opportunities, especially in the Indian context as many languages were spoken in different parts of the Country. A Recommender Model in the Malayalam language in Travel and tourism domain using unsupervised machine learning techniques is the intention behind this paper. Malayalam is a low-resource and highly inflected language that possesses a greater chance for ambiguity. Data sharing online platforms and social media are used as data collection sources, where the availability is still limited and challenging, which may cause scarcity of data. The works propose various methodologies to generate a custom-made scraping model from the social media written in the Malayalam Language and its preprocessing. A deep-level Travelogue Tagger has been specially constructed as part of the experiment. This paper proposes a recommender model based on traveler reviews using Collaborative filtering and Cosine similarity methods. The experiment succeeded with high precision.

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Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Travel Recommender System by Mining Behavioral Attributes From Online Travelogues in Malayalam – A Low Resourced Language

2023

ecb

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“…Shruthi and Pranav (2016) propose another supervised approach based on the TnT tagger (Brants, 2002) and maximum entropy models. A neural network approach is proposed by Ajees and Idicula (2018) who use word embeddings of context words and morphs of the target word as features. A similar system but with a different neural architecture (RNN-LSTM) has also been proposed (Sreeja and Pillai, 2020).…”

Section: Related Workmentioning

confidence: 99%

“…Although it is an under-resourced language, the presence of Malayalam in the form of articles and data repositories on the internet has been growing steadily over the years. It has featured in a limited number of NLP tasks, including morphological analysis (Bhavukam et al, 2018), POS tagging (Akhil et al, 2020) and NER (Ajees and Idicula, 2018). However, many studies use small locally generated data sets (Nambiar et al, 2019) or domain specific data sets (Kumar et al, 2019), (Devi et al, 2016), which usually are not freely available.…”

Section: Malayalammentioning

confidence: 99%

“…ARNEKT IECSIL FIRE 2018 NER Dataset This corpus was compiled from the abstracts and info-box properties from DBpedia for the (IECSIL) shared task (Hullathy Balakrishnan et al, 2018) CUSAT NER Dataset This is a manually annotated NER data set developed by CUSAT. 6 It is based on the CUSAT POS tagged data set for Malayalam (Ajees and Idicula, 2018). About 200,000 words from "internet texts" were manually annotated.…”

Section: Data Sets and Resourcesmentioning

confidence: 99%

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EmployingWikipedia as a Resource for Named Entity Recognition in Morphologically Complex Under-Resourced Languages

Krishnan¹,

Ziehe²,

Pannach³

et al. 2021

Proceedings of the 14thWorkshop on Building and Using Comparable Corpora (BUCC 2021)

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We propose a novel approach for rapid prototyping of named entity recognisers through the development of semi-automatically annotated data sets. We demonstrate the proposed pipeline on two under-resourced agglutinating languages: the Dravidian language Malayalam and the Bantu language isiZulu. Our approach is weakly supervised and bootstraps training data from Wikipedia and Google Knowledge Graph. Moreover, our approach is relatively language independent and can consequently be ported quickly (and hence cost-effectively) from one language to another, requiring only minor language-specific tailoring.

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Online Malayalam Script Assortment and Preprocessing for Building Recommender Systems

Muneer

Basheer

Rizwana

et al. 2022

Smart Innovation, Systems and Technologies

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A Named Entity Recognition System for Malayalam using Neural Networks

Cited by 9 publications

References 4 publications

An Intelligent Travel Recommender System by Mining Behavioral Attributes From Online Travelogues in Malayalam – A Low Resourced Language

An Intelligent Travel Recommender System by Mining Behavioral Attributes From Online Travelogues in Malayalam – A Low Resourced Language

EmployingWikipedia as a Resource for Named Entity Recognition in Morphologically Complex Under-Resourced Languages

Online Malayalam Script Assortment and Preprocessing for Building Recommender Systems

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