Conditional Random Field Based Named Entity Recognition in Geological text

Sobhana, N. V.; Mitra, Pabitra; Ghosh, Sourav

doi:10.5120/72-166

Cited by 45 publications

(17 citation statements)

References 7 publications

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“…1. The CRF model is the model proposed by Sobhana et al [38], using CRF for NER in geosciences. We used the CRF method as our benchmark.…”

Section: Resultsmentioning

confidence: 99%

“…These NER methods provide a useful reference for NER tasks in geoscience. Sobhana et al [38] first used the CRF model combined with some manually designed features (such as prefixes and suffixes for words) to extract 17 types of geoscience-related entities from geoscience texts. Considering named entities in geological hazard literature are diverse in form and complicated in context, it is challenging to design practical features, resulting in a poor performance by CRF models that rely on manually designed features.…”

Section: Related Workmentioning

confidence: 99%

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Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards

Fan

Wang

Yan

et al. 2019

IJGI

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Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing a geological hazard knowledge graph, has to face the challenges that named entities in geological hazard literature are diverse in form, ambiguous in semantics, and uncertain in context. This can introduce difficulties in designing practical features during the NER classification. To address the above problem, this paper proposes a deep learning-based NER model; namely, the deep, multi-branch BiGRU-CRF model, which combines a multi-branch bidirectional gated recurrent unit (BiGRU) layer and a conditional random field (CRF) model. In an end-to-end and supervised process, the proposed model automatically learns and transforms features by a multi-branch bidirectional GRU layer and enhances the output with a CRF layer. Besides the deep, multi-branch BiGRU-CRF model, we also proposed a pattern-based corpus construction method to construct the corpus needed for the deep, multi-branch BiGRU-CRF model. Experimental results indicated the proposed deep, multi-branch BiGRU-CRF model outperformed state-of-the-art models. The proposed deep, multi-branch BiGRU-CRF model constructed a large-scale geological hazard literature knowledge graph containing 34,457 entities nodes and 84,561 relations.

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“…1. The CRF model is the model proposed by Sobhana et al [38], using CRF for NER in geosciences. We used the CRF method as our benchmark.…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards

Fan

Wang

Yan

et al. 2019

IJGI

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“…In [13], the author proposed Conditional Random Field (CRF) to overcome the limitations of HMM and solve the label bias problem. In [14], the author used CRF to handle NER tasks and achieved good results. But the author did not consider whether the connection between characters, words, and positions in the sentence would affect the final result.…”

Section: Introductionmentioning

confidence: 99%

CWPC_BiAtt: Character–Word–Position Combined BiLSTM-Attention for Chinese Named Entity Recognition

2020

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Usually taken as linguistic features by Part-Of-Speech (POS) tagging, Named Entity Recognition (NER) is a major task in Natural Language Processing (NLP). In this paper, we put forward a new comprehensive-embedding, considering three aspects, namely character-embedding, word-embedding, and pos-embedding stitched in the order we give, and thus get their dependencies, based on which we propose a new Character–Word–Position Combined BiLSTM-Attention (CWPC_BiAtt) for the Chinese NER task. Comprehensive-embedding via the Bidirectional Llong Short-Term Memory (BiLSTM) layer can get the connection between the historical and future information, and then employ the attention mechanism to capture the connection between the content of the sentence at the current position and that at any location. Finally, we utilize Conditional Random Field (CRF) to decode the entire tagging sequence. Experiments show that CWPC_BiAtt model we proposed is well qualified for the NER task on Microsoft Research Asia (MSRA) dataset and Weibo NER corpus. A high precision and recall were obtained, which verified the stability of the model. Position-embedding in comprehensive-embedding can compensate for attention-mechanism to provide position information for the disordered sequence, which shows that comprehensive-embedding has completeness. Looking at the entire model, our proposed CWPC_BiAtt has three distinct characteristics: completeness, simplicity, and stability. Our proposed CWPC_BiAtt model achieved the highest F-score, achieving the state-of-the-art performance in the MSRA dataset and Weibo NER corpus.

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“…CRF are used, for instance, in the task of named entity recognition in documents from various fields and areas of knowledge [8][9][10] and text summarization problems [11,12] in which this classifier is applied to distinguish more important sentences.…”

Section: Machine Learning In Nlp: Algorithms Tools and Technologiesmentioning

confidence: 99%

On Usage of Machine Learning for Natural Language Processing Tasks as Illustrated by Educational Content Mining

Melnikov¹,

Botov²,

Klenin³

2017

Ontology of Designing

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In this paper, we review most popular approaches to a variety of natural language processing (NLP) tasks, primarily those, which involve machine learning: from classics to state-of-the-art technologies. Most modern approaches can be separated into three rough categories: ones based on distributional hypothesis, those extracting information from graph-like structures (such as ontologies) and the ones that look for lexico-syntactic patterns in text documents. We focus mainly on the former of the three. Before the analysis can even begin, one of the important steps in preparation stage of NLP is the task of representing words and documents as numeric vectors. There exists a variety of approaches from the most simplistic Bag-of-Words to sophisticated machine learning methods, such as word embedding. Today, in the task of information retrieval the best quality for both English and Russian languages is achieved by approaches based on word embedding algorithms, trained on carefully picked text corpora in conjunction with deep syntactic and semantic analysis using various deep neural networks. A big variety of different machine learning algorithms is being applied for NLP tasks such as Part-of-Speech-tagging, text summarization, named entity recognition, document classification, topic and relation extraction and natural language question answering. We also review possibilities of applying these approaches and methods to educational content analysis, and propose the novel approach to utilizing NLP and machine learning capabilities in analyzing and synthesizing educational content in a form of a decision support systems.

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Conditional Random Field Based Named Entity Recognition in Geological text

Cited by 45 publications

References 7 publications

Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards

Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards

CWPC_BiAtt: Character–Word–Position Combined BiLSTM-Attention for Chinese Named Entity Recognition

On Usage of Machine Learning for Natural Language Processing Tasks as Illustrated by Educational Content Mining

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