Construction and annotation of a corpus of contemporary Nepali

Yadava, Yogendra Prasad; Hardie, Andrew; Lohani, Ram Raj; Regmi, Bhim Narayan; Gurung, Srishtee; Gurung, Amar Bdr; McEnery, Tony; Allwood, Jens; Hall, Patrick A. V.

doi:10.3366/e1749503208000166

Cited by 22 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MATERIALS USED. One of the corpora used for this project is the Nepali National Corpus (NNC) (Yadava et al 2008), which consists of 14 million words from papers, books, and websites spanning a wide variety of topics. The original data consists of works between 1990 through 1992, referred to hereafter as NNC-O (for NNC-Original).…”

Section: Methodsmentioning

confidence: 99%

How revolution and gender dynamics affected Nepali 3rd-person honorification from the 1990s to the 2010s: A corpus analysis

Shrestha

2024

Proc Ling Soc Amer

View full text Add to dashboard Cite

One way to express honorification, by which speakers index relations of respect, is through varying pronouns and verb inflection. This phenomenon has been studied in European languages with binary 2nd-person pronouns (the T-V model). Past research finds that these systems tend to level with social change. This study explores Nepali, which also uses pronouns and verb-inflections. Nepali has 2nd and 3rd-person honorification, with 4 and 3 different levels respectively. The study employs a corpus analysis to investigate Nepali 3rd-person honorification with regards to the fall of monarchy (1996–2010). The paper finds that this social change correlates to 3rd-person honor leveling.

show abstract

Section: Methodsmentioning

confidence: 99%

How revolution and gender dynamics affected Nepali 3rd-person honorification from the 1990s to the 2010s: A corpus analysis

Shrestha

2024

Proc Ling Soc Amer

View full text Add to dashboard Cite

show abstract

“…We train two different types of embeddings, Monolingual and Multilingual. Monolingual embedding is trained on Nepali texts collected only from Nepali National Corpus (NNC) [28]. Multilingual embedding is trained on text collected from NNC + Nepali OSCAR [29] + English texts extracted 4 from latest 169899 articles from Wikipedia dump 5 .…”

Section: Methodsmentioning

confidence: 99%

Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts

Singh

Timilsina

Bal

et al. 2020

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

View full text Add to dashboard Cite

With the increase in internet access and the ease of writing comments in the Nepali language, fine-grained sentiment analysis of social media comments is becoming more and more pertinent. There are a number of benchmarked datasets for high-resource languages (English, French, and German) in specific domains like restaurants, hotels or electronic goods but not in low-resource languages like Nepali. In this paper, we present our work to create a dataset for the targeted aspect-based sentiment analysis in the social media domain, set up a dataset benchmark and evaluate using various machine learning models. The dataset comprises of code-mixed and code-switched comments extracted from Nepali YouTube videos. We present convincing baselines using a multilingual BERT model for the Aspect Term Extraction task and BiLSTM model for the Sentiment Classification Task achieving 57.978% and 81.60% F1 score respectively.

show abstract

“…They also annotated their dataset for POS tagging but didn't perform manual annotations for it. They used the Nepali National Corpus (NCC) [40] to train a BiLSTM model and used the model to annotate the NepaliNER dataset with POS tags. Adding to the contribution in datasets, Sitaula et al [41] designed a dataset by crawling news documents from popular Nepali online news portals like Kantipur, Ratopati, and Nagarik to collect a total of 35,651 documents across 17 news categories.…”

Section: The Pressing Need Of Data In Nepali Lan-guagementioning

confidence: 99%

Multi-Aspect Annotation and Analysis of Nepali Tweets on Anti-Establishment Election Discourse

Rauniyar,

Poudel,

Shiwakoti

et al. 2023

IEEE Access

View full text Add to dashboard Cite

In today's social media-dominated landscape, digital platforms wield substantial influence over public opinion, particularly during crucial political events such as electoral processes. These platforms become hubs for diverse discussions, encompassing topics, reforms, and desired changes. Notably, in times of government dissatisfaction, they serve as arenas for anti-establishment discourse, highlighting the need to analyze public sentiment in these conversations. However, the analysis of such discourse is notably scarce, even in high-resource languages, and entirely non-existent in the context of the Nepali language. To address this critical gap, we present Nepal Anti Establishment discourse Tweets (NAET), a novel dataset comprising 4,445 multi-aspect annotated Nepali tweets, facilitating a comprehensive understanding of political conversations. Our contributions encompass evaluating tweet relevance, sentiment, and satire, while also exploring the presence of hate speech, identifying its targets, and distinguishing directed and non-directed expressions. Additionally, we investigate hope speech, an underexplored aspect crucial in the context of antiestablishment discourse, as it reflects the aspirations and expectations from new political figures and parties. Furthermore, we set NLP-based baselines for all these tasks. To ensure a holistic analysis, we also employ topic modeling, a powerful technique that helps us identify and understand the prevalent themes and patterns emerging from the discourse. Our research thus presents a comprehensive and multi-faceted perspective on anti-establishment election discourse in a low-resource language setting. The dataset is publicly available, facilitating in-depth analysis of political tweets in Nepali discourse and further advancing NLP research for the Nepali language through labeled data and baselines for various NLP tasks. The dataset for this work is made available at https://github.com/rkritesh210/NAET.

show abstract

Construction and annotation of a corpus of contemporary Nepali

Cited by 22 publications

References 12 publications

How revolution and gender dynamics affected Nepali 3rd-person honorification from the 1990s to the 2010s: A corpus analysis

How revolution and gender dynamics affected Nepali 3rd-person honorification from the 1990s to the 2010s: A corpus analysis

Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts

Multi-Aspect Annotation and Analysis of Nepali Tweets on Anti-Establishment Election Discourse

Contact Info

Product

Resources

About