Named-Entity Recognition for Indonesian Language using Bidirectional LSTM-CNNs

Wang, Gunawan; Suhartono, Derwin; Purnomo, Fredy; Ongko, Andrew

doi:10.1016/j.procs.2018.08.193

Cited by 44 publications

(36 citation statements)

References 10 publications

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“…There are some issues about it including existing Indonesian NER research relatively few (Wibawa & Purwarianti, 2016). The Indonesia experiment of NER is not that good compared to English (Gunawan et al, 2018). Recent research was done by (Sukardi et al, 2020) using the neural network combination using BiLSTM and CNN.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…Recent research was done by (Sukardi et al, 2020) using the neural network combination using BiLSTM and CNN. Indonesia NER researches dominate to recognize the named entity of PERSON, ORGANISATION, LOCATION (Syaifudin & Nurwidyantoro, 2016;Gunawan et. al., 2018;Ikhwantri, 2019) including MISC (Gunawan et al, 2018) and EVENT (Ikhwantri, 2019).…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…Indonesia NER researches dominate to recognize the named entity of PERSON, ORGANISATION, LOCATION (Syaifudin & Nurwidyantoro, 2016;Gunawan et. al., 2018;Ikhwantri, 2019) including MISC (Gunawan et al, 2018) and EVENT (Ikhwantri, 2019). (Wibawa & Purwarianti, 2016) introduces 11 new classes of a named entity that can be used in Indonesian NER, i.e., God, Facility, Product, etc.…”

Section: Motivation and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Building the Knowledge Graph for Zakat (KGZ) in Indonesian Language

Sukmana¹,

Muslimin²,

Firmansyah³

et al. 2021

ASMScJ

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In Indonesia, philanthropy is identical to Zakat. Zakat belongs to a specific domain because it has its characteristics of knowledge. This research studied knowledge graph in the Zakat domain called KGZ which is conducted in Indonesia. This area is still rarely performed, thus it becomes the first knowledge graph for Zakat in Indonesia. It is designed to provide basic knowledge on Zakat and managing the Zakat in Indonesia. There are some issues with building KGZ, firstly, the existing Indonesian named entity recognition (NER) is non-restricted and general-purpose based which data is obtained from a general source like news. Second, there is no dataset for NER in the Zakat domain. We define four steps to build KGZ, involving data acquisition, extracting entities and their relationship, mapping to ontology, and deploying knowledge graphs and visualizations. This research contributed a knowledge graph for Zakat (KGZ) and a building NER model for Zakat, called KGZ-NER. We defined 17 new named entity classes related to Zakat with 272 entities, 169 relationships and provided labelled datasets for KGZ-NER that are publicly accessible. We applied the Indonesian-Open Domain Information Extractor framework to process identifying entities’ relationships. Then designed modeling of information using resources description framework (RDF) to build the knowledge base for KGZ and store it to GraphDB, a product from Ontotext. This NER model has a precision 0.7641, recall 0.4544, and F1-score 0.5655. The increasing data size of KGZ is required to discover all of the knowledge of Zakat and managing Zakat in Indonesia. Moreover, sufficient resources are required in future works.

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Section: Motivation and Related Workmentioning

confidence: 99%

Section: Motivation and Related Workmentioning

confidence: 99%

Section: Motivation and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Building the Knowledge Graph for Zakat (KGZ) in Indonesian Language

Sukmana¹,

Muslimin²,

Firmansyah³

et al. 2021

ASMScJ

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“…Most traditional sequence label tagging has used supervised learning techniques such as CRFs [17], hidden Markov models [18], or maximum entropy Markov models [19]. However, model sequence label tagging has used neural network architectures instead, because of their effectiveness [20]- [23]. Here, we explain the structure of a recurrent neuron and present the background of our Bi-LSTM (an improvement on RNNs) and how we apply CRFs for POS and NER tagging.…”

Section: Tagging Pos and Ner Using Bi-directional Lstm-crfsmentioning

confidence: 99%

Korean-Vietnamese Neural Machine Translation with Named Entity Recognition and Part-of-Speech Tags

Nguyen

et al. 2020

IEICE Trans. Inf. & Syst.

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Since deep learning was introduced, a series of achievements has been published in the field of automatic machine translation (MT). However, Korean-Vietnamese MT systems face many challenges because of a lack of data, multiple meanings of individual words, and grammatical diversity that depends on context. Therefore, the quality of Korean-Vietnamese MT systems is still sub-optimal. This paper discusses a method for applying Named Entity Recognition (NER) and Part-of-Speech (POS) tagging to Vietnamese sentences to improve the performance of Korean-Vietnamese MT systems. In terms of implementation, we used a tool to tag NER and POS in Vietnamese sentences. In addition, we had access to a Korean-Vietnamese parallel corpus with more than 450K paired sentences from our previous research paper. The experimental results indicate that tagging NER and POS in Vietnamese sentences can improve the quality of Korean-Vietnamese Neural MT (NMT) in terms of the BiLingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score. On average, our MT system improved by 1.21 BLEU points or 2.33 TER scores after applying both NER and POS tagging to the Vietnamese corpus. Due to the structural features of language, the MT systems in the Korean to Vietnamese direction always give better BLEU and TER results than translation machines in the reverse direction.

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“…This is also applied for Indonesian language. Thus, there are already several researches for Indonesian NE tagger [1,2,3,4,5,6]. Most researches on Indonesian NER employed traditional machine learning algorithms such as association rule [1], ensemble learning [4], and support vector machine (SVM) [2,3,5].…”

Section: Introductionmentioning

confidence: 99%

Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger

Hoesen¹,

Purwarianti

2018

2018 International Conference on Asian Language Processing (IALP)

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Researches on Indonesian named entity (NE) tagger have been conducted since years ago. However, most did not use deep learning and instead employed traditional machine learning algorithms such as association rule, support vector machine, random forest, naïve bayes, etc. In those researches, word lists as gazetteers or clue words were provided to enhance the accuracy. Here, we attempt to employ deep learning in our Indonesian NE tagger. We use long short-term memory (LSTM) as the topology since it is the state-of-the-art of NE tagger. By using LSTM, we do not need a word list in order to enhance the accuracy. Basically, there are two main things that we investigate. The first is the output layer of the network: Softmax vs conditional random field (CRF). The second is the usage of part of speech (POS) tag embedding input layer. Using 8400 sentences as the training data and 97 sentences as the evaluation data, we find that using POS tag embedding as additional input improves the performance of our Indonesian NE tagger. As for the comparison between Softmax and CRF, we find that both architectures have a weakness in classifying an NE tag.

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Named-Entity Recognition for Indonesian Language using Bidirectional LSTM-CNNs

Cited by 44 publications

References 10 publications

Building the Knowledge Graph for Zakat (KGZ) in Indonesian Language

Building the Knowledge Graph for Zakat (KGZ) in Indonesian Language

Korean-Vietnamese Neural Machine Translation with Named Entity Recognition and Part-of-Speech Tags

Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger

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