Multiclass Event Classification from Text

Ali, Daler; Missen, Malik Muhammad Saad; Husnain, Mujtaba

doi:10.1155/2021/6660651

Cited by 18 publications

(13 citation statements)

References 41 publications

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“…After that, the existing event library was updated. Ali et al [70] presented a sentence-level multiclass event detection model for the Urdu language. They used deep learning models and word embedding, one-hot encoding, TF, TF/IDF, and dynamic embedding-based feature vectors to evaluate their model's performance.…”

Section: Deep-machine-learning-based Approachesmentioning

confidence: 99%

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A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data

Mredula

Dey

Rahman

et al. 2022

Sensors

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Social media platforms have many users who share their thoughts and use these platforms to organize various events collectively. However, different upsetting incidents have occurred in recent years by taking advantage of social media, raising significant concerns. Therefore, considerable research has been carried out to detect any disturbing event and take appropriate measures. This review paper presents a thorough survey to acquire in-depth knowledge about the current research in this field and provide a guideline for future research. We systematically review 67 articles on event detection by sensing social media data from the last decade. We summarize their event detection techniques, tools, technologies, datasets, performance metrics, etc. The reviewed papers mainly address the detection of events, such as natural disasters, traffic, sports, real-time events, and some others. As these detected events can quickly provide an overview of the overall condition of the society, they can significantly help in scrutinizing events disrupting social security. We found that compatibility with different languages, spelling, and dialects is one of the vital challenges the event detection algorithms face. On the other hand, the event detection algorithms need to be robust to process different media, such as texts, images, videos, and locations. We outline that the event detection techniques compatible with heterogeneous data, language, and the platform are still missing. Moreover, the event and its location with a 24 × 7 real-time detection system will bolster the overall event detection performance.

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Section: Deep-machine-learning-based Approachesmentioning

confidence: 99%

“…Generated features were fed into the embedding layer, and output from the embedding layer was fed into the neural network's fully connected/dense layer. [70] Unigram and bigram tokens Urdu Accuracy, precision, recall, and F1…”

Section: Deep-machine-learning-based Approachesmentioning

confidence: 99%

A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data

Mredula

Dey

Rahman

et al. 2022

Sensors

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“…In comparison, we used sentences of Urdu language for classification and explored the textual features of sentences. A multiclass event classification task [18] was performed for Urdu language text that evaluated the performance of different classifiers. On the contrary, we evaluated the performance of Random Forest classifiers for different level of n-gram features.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentence Classification Using N-Grams in Urdu Language Text

Awan

Ali

Samad

et al. 2021

Scientific Programming

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The usage of local languages is being common in social media and news channels. The people share the worthy insights about various topics related to their lives in different languages. A bulk of text in various local languages exists on the Internet that contains invaluable information. The analysis of such type of stuff (local language’s text) will certainly help improve a number of Natural Language Processing (NLP) tasks. The information extracted from local languages can be used to develop various applications to add new milestone in the field of NLP. In this paper, we presented an applied research task, “multiclass sentence classification for Urdu language text at sentence level existing on the social networks, i.e., Twitter, Facebook, and news channels by using N-grams features.” Our dataset consists of more than 1,00000 instances of twelve (12) different types of topics. A famous machine learning classifier Random Forest is used to classify the sentences. It showed 80.15%, 76.88%, and 64.41% accuracy for unigram, bigram, and trigram features, respectively.

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“…Du and Duan developed the English phrase recognition system using continuous speech recognition and word tree algorithms [21]. Ali et al [22] developed a database of 103965 labelled sentences in Urdu language. ese sentences are classified using the convolution neural network, recurrence neural network, and deep neural network to extract event information from these languages.…”

Section: Dataset Development Processmentioning

confidence: 99%

Offline Pashto Characters Dataset for OCR Systems

Khan

Nazir

2021

Security and Communication Networks

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In computer vision and artificial intelligence, text recognition and analysis based on images play a key role in the text retrieving process. Enabling a machine learning technique to recognize handwritten characters of a specific language requires a standard dataset. Acceptable handwritten character datasets are available in many languages including English, Arabic, and many more. However, the lack of datasets for handwritten Pashto characters hinders the application of a suitable machine learning algorithm for recognizing useful insights. In order to address this issue, this study presents the first handwritten Pashto characters image dataset (HPCID) for the scientific research work. This dataset consists of fourteen thousand, seven hundred, and eighty-four samples—336 samples for each of the 44 characters in the Pashto character dataset. Such samples of handwritten characters are collected on an A4-sized paper from different students of Pashto Department in University of Peshawar, Khyber Pakhtunkhwa, Pakistan. On total, 336 students and faculty members contributed in developing the proposed database accumulation phase. This dataset contains multisize, multifont, and multistyle characters and of varying structures.

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Multiclass Event Classification from Text

Cited by 18 publications

References 41 publications

A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data

A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data

Sentence Classification Using N-Grams in Urdu Language Text

Offline Pashto Characters Dataset for OCR Systems

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