The GENIA corpus

Ohta, Tomoko; Tateisi, Yuka; Kim, Jin-Dong

doi:10.3115/1289189.1289260

Cited by 100 publications

(18 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GENIA. GENIA (Ohta et al, 2002) is an English nested NER dataset in the molecular biology domain containing five entity types (e.g., DNA and RNA). More details (including entity types, sentence number, and examples) of three nested NER datasets ACE2004, ACE2005, and GENIA can be found in Appendix A.2.…”

Section: Results On Nested Nermentioning

confidence: 99%

Atlas of dynamic spectra of fast radio burst FRB 20201124A

Bo-Jun¹,

Xu²,

Jiang³

et al. 2023

Chinese Phys. B

View full text Add to dashboard Cite

Fast radio bursts (FRBs) are highly dispersed millisecond-duration radio bursts [1,2], of which the physical origin is still not fully understood. FRB 20201124A is one of the most actively repeating FRBs. In this paper, we present the collection of 1863 burst dynamic spectra of FRB 20201124A measured with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The current collection, taken from the observation during the FRB active phase from April to June 2021, is the largest burst sample detected in any FRB so far. The standard PSRFITs format is adopted, including dynamic spectra of the burst, and the time information of the dynamic spectra, in addition, mask files help readers to identify the pulse positions are also provided. The dataset is available in Science Data Bank, with the link https://www.doi.org/10.57760/sciencedb.j00113.00076.

show abstract

Section: Results On Nested Nermentioning

confidence: 99%

Atlas of dynamic spectra of fast radio burst FRB 20201124A

Bo-Jun¹,

Xu²,

Jiang³

et al. 2023

Chinese Phys. B

View full text Add to dashboard Cite

show abstract

“…'47 kDa sterol regulatory element binding factor'. In GENIA V3.0 (Ohta et al, 2002), we find that 18.6% of biomedical entity names consist of at least four words, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 90%

“…All of our experiments are done on GENIA corpus, which is the largest annotated corpus in the molecular biology domain available to public (Ohta et al, 2002). In our experiments, three versions are used.…”

Section: Genia Corpusmentioning

confidence: 99%

Machine Learning Approach

Roopashree

Anitha²,

Madhumathy

2021

Advances in Data Mining and Database Management

View full text Add to dashboard Cite

Ayurveda medicines uses herbs for curing many ailments without side effects. The biggest concern related to Ayurveda medicine is extinction of many important medicinal herbs, which may be due to insufficient knowledge, weather conditions, and urbanization. Another reason consists of lack of online facts on Indian herbs because it is dependent on books and experts. This concern has motivated in utilizing the machine learning techniques to identify and reveal few details of Indian medicinal herbs because, until now, it is identified manually, which is cumbersome and may lead to errors. Many researchers have shown decent results in identifying and classifying plants with good accuracy and robustness. But no complete framework and strong evidence is projected on Indian medicinal herbs. Accordingly, the chapter aims to provide an outline on how machine learning techniques can be adopted to enrich the knowledge of Indian herbs, which advantages both common man and the domain experts with wide information on traditional herbs.

show abstract

“…Datasets We evaluate our method on English and Chinese NER datasets. English datasets include the general domain flat NER CoNLL2003 (Tjong Kim Sang and De Meulder, 2003), the nested NER ACE2005 (Kirkpatrick, 2010), and the biomedical nested NER GENIA (Ohta et al, 2002). Chinese datasets include four commonly used general domain flat NER benchmarks Resume (Zhang and Yang, 2018), Weibo (Peng and Dredze, 2015), MSRA (Levow, 2006) Inference setup For all generative models, we use greedy search with a beam size of 1, a maximum of 512 new tokens, and a temperature of 1.0.…”

Section: Setupmentioning

confidence: 99%

Serial and Parallel Recurrent Convolutional Neural Networks for Biomedical Named Entity Recognition

Yang

et al. 2019

Database Systems for Advanced Applications

View full text Add to dashboard Cite

In this study, we aim to reduce generation latency for Named Entity Recognition (NER) with Large Language Models (LLMs). The main cause of high latency in LLMs is the sequential decoding process, which autoregressively generates all labels and mentions for NER, significantly increase the sequence length. To this end, we introduce Parallel Decoding in LLM for NER (PaDeLLM-NER), a approach that integrates seamlessly into existing generative model frameworks without necessitating additional modules or architectural modifications. PaDeLLM-NER allows for the simultaneous decoding of all mentions, thereby reducing generation latency. Experiments reveal that PaDeLLM-NER significantly increases inference speed that is 1.76 to 10.22 times faster than the autoregressive approach for both English and Chinese. Simultaneously it maintains the quality of predictions as evidenced by the performance that is on par with the state-of-the-art across various datasets.* Code is available at URL masked for anonymous review.* https://huggingface.co/meta-llama/Llama-2-7b * https://huggingface.co/baichuan-inc/ Baichuan2-7B-Base

show abstract

The GENIA corpus

Cited by 100 publications

References 5 publications

Atlas of dynamic spectra of fast radio burst FRB 20201124A

Atlas of dynamic spectra of fast radio burst FRB 20201124A

Machine Learning Approach

Serial and Parallel Recurrent Convolutional Neural Networks for Biomedical Named Entity Recognition

Contact Info

Product

Resources

About