poliWeet — Election prediction tool using tweets

Mohd, Mudasir; Javeed, Saheeba; Wani, Mohsin Altaf; Khanday, Hilal Ahmad; Wani, Abid Hussain; Mir, Umar Bashir; Sheikh, Nasrullah

doi:10.1016/j.simpa.2023.100542

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Word embeddings are high-dimensional real-valued vectors generated for each word. They have been found to be helpful in NLP tasks such as text classification, sentiment analysis [ 49 ], and machine translation. In high-dimensional vector spaces, these representations and their geometric properties can help determine the coherence of various word usages.…”

Section: Methodsmentioning

confidence: 99%

Biomedical semantic text summarizer

Kirmani,

Kour,

Mohd

et al. 2024

BMC Bioinformatics

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Background Text summarization is a challenging problem in Natural Language Processing, which involves condensing the content of textual documents without losing their overall meaning and information content, In the domain of bio-medical research, summaries are critical for efficient data analysis and information retrieval. While several bio-medical text summarizers exist in the literature, they often miss out on an essential text aspect: text semantics. Results This paper proposes a novel extractive summarizer that preserves text semantics by utilizing bio-semantic models. We evaluate our approach using ROUGE on a standard dataset and compare it with three state-of-the-art summarizers. Our results show that our approach outperforms existing summarizers. Conclusion The usage of semantics can improve summarizer performance and lead to better summaries. Our summarizer has the potential to aid in efficient data analysis and information retrieval in the field of biomedical research.

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Section: Methodsmentioning

confidence: 99%