A comprehensive review on feature set used for anaphora resolution

Lata, Kusum; Singh, Pardeep; Dutta, Kamlesh

doi:10.1007/s10462-020-09917-3

Cited by 14 publications

(5 citation statements)

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“…A characteristic can be insignificant, significant, or redundant. Various feature selection approaches are used to eliminate irrelevant and superfluous characteristics (Ahmad et al 2019b;Lata et al 2020). Feature Selection is a procedure that identifies and eliminates superfluous and irrelevant characteristics from the feature list and thus increases sentiment classification accuracy.…”

Section: Feature Selection Approachmentioning

confidence: 99%

A survey on sentiment analysis methods, applications, and challenges

2022

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The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis is the process of gathering and analyzing people's opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People's opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. However, the sentiment analysis and evaluation procedure face numerous challenges. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity. Sentiment analysis identifies and extracts subjective information from the text using natural language processing and text mining. This article discusses a complete overview of the method for completing this task as well as the applications of sentiment analysis. Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. Finally, the challenges of sentiment analysis are examined in order to define future directions.

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Section: Feature Selection Approachmentioning

confidence: 99%

A survey on sentiment analysis methods, applications, and challenges

2022

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“…While English is the main language where researchers made progress in this area, this issue has also been researched in various languages such as Arabic, Chinese, Spanish, German, Dutch, Catalan, Italian, and Russian. There are many shared task datasets such as ONTONOTES, CoNLL-2011/2012 exist for the English language prominently as discussed by authors [6][7][8]. There exists significant work on the Coreference resolution system for the English language.…”

Section: Sh1mentioning

confidence: 99%

“…The input data for the presented experiment is coreference annotated data for the Hindi language [33] containing 3.6K sentences and 78K tokens consisting of news article domains. The data is collected online from the website IITH 6 . This dataset contains coreference chain created automatically.…”

Section: Data Preparationmentioning

confidence: 99%

“…Coreference Resolution for English: Recently, many researchers have done a survey in-depth for coreference resolution [6][7][8]. Many different approaches are utilized for the task of coreference resolution.…”

Section: Related Workmentioning

confidence: 99%

“…𝜙 𝑘, 𝑛 is the feature vector of the distance between the mention pair (𝑚𝑠 𝑘 , 𝑚𝑠 𝑛 . The final step is to calculate the mention pairwise score, i-e., 𝑠𝑐𝑜𝑟𝑒 𝑘, 𝑛 as shown in equation(6).…”

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confidence: 99%

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DeepHCoref: A Deep Neural Coreference Resolution for Hindi Text

Lata

Singh

Dutta

2022

Preprint

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Coreference Resolution is the process of detecting a cluster of mentions that point to the same entity. The Coreference Resolution will enhance the performance of numerous Natural Language Processing (NLP) applications viz. Machine Translation, Question Answering, Chatbots, Text Summarization, etc. The state-of-the-art Coreference Resolution model for Hindi is based on a Rule-based and machine learning approach. This paper presents the Coreference Resolution system for Hindi based on Bi-GRU-CNN and Biaffine classifier with IndicBERT and MuRIL BERT. According to our knowledge, the first Coreference resolution system based on deep learning is developed, particularly for Hindi. The motivation behind this is the scarcity of resources available for Hindi and to diminish the dominion of hand-crafted features used by the previous Coreference resolution model in the Hindi language and present a new state-of-the-art Coreference Resolution model for Hindi. The coreference annotated dataset is used for the Hindi language, containing 3.6K verbalizations and 78K tokens from the news article domain. There is a lot of work done in this field for English, but minimal amount of work is presented in Hindi. The experimental results received are commendable regarding the Precision, Recall, and F-measure.

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