Named Entity Recognition for Vietnamese Real Estate Advertisements

Huynh, Son T.; Le, Khiem H.; Dang, Ngoc T.; Le, Bao Quang Gia; Huynh, Dang; Nguyen, Binh T.; Nguyễn, Trung Thành; Ho, Nhi

doi:10.1109/nics54270.2021.9701519

Cited by 4 publications

(8 citation statements)

References 14 publications

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“…Regarding more specialized types of entities in the Vietnamese language, Truong and his team [15] investigated several methods, including PhoBERT-based [16] for extracting information regarding COVID-19 patients. In [2], Named Entity Recognition in the Vietnamese real estate domain was investigated, albeit on a smaller scale with fewer data in both quantity and variety and without addressing the challenges of low-quality data sources, which we deal with by noise filtering. It is worth noting that, among the mentioned studies, [10] gave a brief example illustrating the monosyllabic nature of the Vietnamese language, its impact on NER, and why one could employ word segmentation for such tasks.…”

Section: A Named Entity Recognition For the Vietnamese Languagementioning

confidence: 99%

“…This research develops a NER task for Vietnamese real estate advertisement posts with the IOB format (short for inside, outside, beginning). We investigate this task through various experiments on our annotated dataset: (i) the capabilities of traditional models including MishWindowEn-coderW300 [2], BiLSTM-CRF [26] (ii) the effectiveness of the pre-trained language models such as the variations of Transformers [27].…”

Section: Ner-reap: Our Proposed Named Entity Recognition Model For Re...mentioning

confidence: 99%

“…the first model is MishWindowEncoderW300, this is the model is proposed by spacy 17 . In experiments, we set up the same hyperparameters proposed by Son et al [2]. The second model is BiLSTM-CRF [26] which is the standard technique for the NER task, which was first released in 2018 by Rrubaa and Aravindh.…”

Section: ) Traditional Modelsmentioning

confidence: 99%

“…In 2012, [1] proposed a rule-based information extraction system, which heavily depended on gazetteers and faced difficulties with diverse writing styles, very long entities, or improper capitalization, as was noted in their error analysis. In 2021, Binh and colleagues [2] conducted experiments for named entity recognition for Vietnamese real estate data collected from three websites, but they did not correctly address noisy data. Additionally, in comparison with [2], our study includes more experiments with additional methods, a more extensive and diverse dataset, and a platform for analytical applications.…”

Section: Introductionmentioning

confidence: 99%

“…In 2021, Binh and colleagues [2] conducted experiments for named entity recognition for Vietnamese real estate data collected from three websites, but they did not correctly address noisy data. Additionally, in comparison with [2], our study includes more experiments with additional methods, a more extensive and diverse dataset, and a platform for analytical applications.…”

Section: Introductionmentioning

confidence: 99%

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An End-to-End Named Entity Recognition Platform for Vietnamese Real Estate Advertisement Posts and Analytical Applications

Nguyen

Doan²,

Huynh

et al. 2022

IEEE Access

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The volume and complexity of publicly available real estate data have been snowballing. As a result, information extraction and processing have become increasingly challenging and essential for many PropTech (Property Technology) companies worldwide. The challenges are even more pronounced with languages other than English, such as Vietnamese, where few studies in this field have taken place. This paper presents an end-to-end framework for automatically collecting real estate advertisement posts from different data sources, extracting useful information, and storing computed data into proper data warehouses and data marts for the Vietnamese advertisement posts in real estate. After that, one can serve aggregated data for other descriptive and predictive analytics. We combine two models for constructing the most appropriate extraction step: Noise Filtering and Named Entity Recognition (NER). These models can help process initial input data and extract all helpful information. The experiment results show that using PhoBERT large can achieve the best performance compared to other approaches. Furthermore, we can obtain the corresponding F1 scores of the Noise filtering module and the NER module as 0.8697 and 0.8996, respectively. Finally, we utilize Superset for implementing analytic dashboards to visualize the predicted results and serve for further analysis and management processes.

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Section: A Named Entity Recognition For the Vietnamese Languagementioning

confidence: 99%

Section: Ner-reap: Our Proposed Named Entity Recognition Model For Re...mentioning

confidence: 99%

Section: ) Traditional Modelsmentioning

confidence: 99%