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बर्मेली O
भाषा O
मात्रै O
जान्ने O
अनिल B-Person
की O
श्रीमती O
अनि O
नेपाली O
, O
अंग्रे O
जी O
र O
थोडाबहुत O
हिन्दी O
समझने O
लेकिन O
बोल्न O
उस्तो O
नआउने O
हामी O
। O

विवाह O
को O
कुरा O
ले O
उन O
लाई O
शैशवकाल O
मा O
पुर्याउँदै O
थियो O
। O

अर्चना B-Person
: O
तपाई O
ले O
अघि O
भन्नु O
भयो O
की O
तपाई O
को O
सिने O
मा O
अन्य O
मूलधारका O
भन्दा O
फरक O
हुन्छ O
। O

अर् O
की O
छोरी O
को O
पनि O
क्यान्सर O
को O
कारण O
ले O
मृत्यु O
भएको O
धेरै O
भएको O
छैन O
। O

बनेपाली B-Person
ले O
चित्र O
बनाइरहेका O
बेला O
स्थानीय O
ले O
गोलो O
घेरा O
बनाएर O
आफ्नो O
चोक O
लाई O
क्यानभास O
मा O
हेर्दै O
थिए O
। O

नवलपुर B-Location
को O
पहाडी O
गाउँ O
डेढगाउँ B-Location
मा O
हाम्रो O
कूलका O
चार O
घर O
मात्र O

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EverestNER - The Benchmark Data Set for Nepali NER

We have created the largest human annotated Named Entity Recognition (NER) data set for Nepali available to date. Highlights:

EverestNER covers five named entities - Person Name, Location, Organization, Event and Date.
EverestNER produces high quality annotations through clear annotation guidelines.
EverestNER has 24,587 entities, 308,353 tokens corresponding to 15,798 sentences.
We split the EverestNER data set into EverestNER-train and EverestNER-test. These standard data sets, therefore, become the first benchmark data sets for evaluating Nepali NER systems.
We report a comprehensive evaluation of state-of-the-art Neural and Transformer models using these data sets. This is the first study to apply BERT model for mining Named Entities for Nepali.
We also discuss the remaining challenges for discovering NEs for Nepali (see our paper below).

Data Set Stats

Data	Articles	Sentences	Tokens	Avg. Sent.Len	LOC	ORG	PER	EVT	DAT
Train	847	13,848	268,741	19.40	5,148	4,756	7,707	312	3,394
Test	149	1,950	39,612	20.31	809	715	1,115	59	572
Total	996	15,798	308,353	19.51	5,957	5,471	8,822	371	3,966

Data Format

The EverestNER data set is divided into train (EverestNER-train) and test (EverestNER-test) sets. Each data set has character level as well as token leven annotations. Please read our paper to get more information on this.

Character Level
- Train: EverestNER-train-char.txt
- Test: EverestNER-test-char.txt
Token Level
- Train: EverestNER-train-bio.txt
- Test: EverestNER-test-bio.txt

Our Results

Model comparision on EverestNER-test. Models (a) baseline (rule-based), (b) BLSTM CRF, and (c) multilingual BERT:

Model	Pre.	Rec.	F1-micro
Baseline (Rule-based)	0.71	0.55	0.62
BLSTM-CRF-wc.ft	0.89	0.74	0.81
BERT-bbmu	0.87	0.84	0.85

Performance evaluation of the best performing model (BERT-bbmu) per named entities:

Model	Pre.	Rec.	F1	Support
PER	0.90	0.85	0.88	1115
LOC	0.85	0.80	0.82	809
ORG	0.85	0.83	0.84	715
EVT	0.46	0.42	0.44	59
DAT	0.91	0.91	0.91	572

License

Non-commercial purposes only. For commercial usages, permissions must be taken from the authors and the relevant parties. See the contact address below.

Unless required by applicable law or agreed to in writing, software and data distributed here is on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

Cite Our Work

If you use the EverestNER data set, please cite our publication:

@inproceedings{niraula2022named,
  title={Named Entity Recognition for Nepali: Data Sets and Algorithms},
  author={Niraula, Nobal and Chapagain, Jeevan},
  booktitle={The International FLAIRS Conference Proceedings},
  volume={35},
  year={2022}
}