scholarly journals An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 79 ◽  
Author(s):  
Xiaoyu Han ◽  
Yue Zhang ◽  
Wenkai Zhang ◽  
Tinglei Huang
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Additional Information ◽  
Named Entity ◽  
Proposed Model ◽  
The Relationship ◽  
Crucial Part

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.

scholarly journals The sale of heritage on eBay: Market trends and cultural value

2020 ◽  
Vol 7 (2) ◽  
pp. 205395172096886
Author(s):  
Mark Altaweel ◽  
Tasoula Georgiou Hadjitofi
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Cultural Value ◽  
Named Entity ◽  
Online Marketplace ◽  
Large Scale Analysis ◽  
Market Trends

The marketisation of heritage has been a major topic of interest among heritage specialists studying how the online marketplace shapes sales. Missing from that debate is a large-scale analysis seeking to understand market trends on popular selling platforms such as eBay. Sites such as eBay can inform what heritage items are of interest to the wider public, and thus what is potentially of greater cultural value, while also demonstrating monetary value trends. To better understand the sale of heritage on eBay’s international site, this work applies named entity recognition using conditional random fields, a method within natural language processing, and word dictionaries that inform on market trends. The methods demonstrate how Western markets, particularly the US and UK, have dominated sales for different cultures. Roman, Egyptian, Viking (Norse/Dane) and Near East objects are sold the most. Surprisingly, Cyprus and Egypt, two countries with relatively strict prohibition against the sale of heritage items, make the top 10 selling countries on eBay. Objects such as jewellery, statues and figurines, and religious items sell in relatively greater numbers, while masks and vessels (e.g. vases) sell at generally higher prices. Metal, stone and terracotta are commonly sold materials. More rare materials, such as those made of ivory, papyrus or wood, have relatively higher prices. Few sellers dominate the market, where in some months 40% of sales are controlled by the top 10 sellers. The tool used for the study is freely provided, demonstrating benefits in an automated approach to understanding sale trends.

scholarly journals Multi-task learning for Chinese clinical named entity recognition with external knowledge

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ming Cheng ◽  
Shufeng Xiong ◽  
Fei Li ◽  
Pan Liang ◽  
Jianbo Gao
Keyword(s):  
Large Scale ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Data Driven ◽  
Named Entity ◽  
Proposed Model ◽  
Comparison Results ◽  
Benchmark Datasets ◽  
Public Datasets ◽  
F Measure

Abstract Background Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records. Most previous methods have been purely data-driven, requiring high-quality and large-scale labeled medical data. However, labeled data is expensive to obtain, and these data-driven methods are difficult to handle rare and unseen entities. Methods To tackle these problems, this study presents a novel multi-task deep neural network model for Chinese NER in the medical domain. We incorporate dictionary features into neural networks, and a general secondary named entity segmentation is used as auxiliary task to improve the performance of the primary task of named entity recognition. Results In order to evaluate the proposed method, we compare it with other currently popular methods, on three benchmark datasets. Two of the datasets are publicly available, and the other one is constructed by us. Experimental results show that the proposed model achieves 91.07% average f-measure on the two public datasets and 87.05% f-measure on private dataset. Conclusions The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models.

scholarly journals A Sequence-to-Set Network for Nested Named Entity Recognition

2021 ◽  
Author(s):  
Zeqi Tan ◽  
Yongliang Shen ◽  
Shuai Zhang ◽  
Weiming Lu ◽  
Yueting Zhuang
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Search Space ◽  
Entity Recognition ◽  
Bipartite Matching ◽  
Named Entity ◽  
Proposed Model ◽  
Fixed Set ◽  
Sequence Method

Named entity recognition (NER) is a widely studied task in natural language processing. Recently, a growing number of studies have focused on the nested NER. The span-based methods, considering the entity recognition as a span classification task, can deal with nested entities naturally. But they suffer from the huge search space and the lack of interactions between entities. To address these issues, we propose a novel sequence-to-set neural network for nested NER. Instead of specifying candidate spans in advance, we provide a fixed set of learnable vectors to learn the patterns of the valuable spans. We utilize a non-autoregressive decoder to predict the final set of entities in one pass, in which we are able to capture dependencies between entities. Compared with the sequence-to-sequence method, our model is more suitable for such unordered recognition task as it is insensitive to the label order. In addition, we utilize the loss function based on bipartite matching to compute the overall training loss. Experimental results show that our proposed model achieves state-of-the-art on three nested NER corpora: ACE 2004, ACE 2005 and KBP 2017. The code is available at https://github.com/zqtan1024/sequence-to-set.

scholarly journals A Hierarchical Multi-Task Approach for Learning Embeddings from Semantic Tasks

2019 ◽  
Vol 33 ◽  
pp. 6949-6956 ◽  
Author(s):  
Victor Sanh ◽  
Thomas Wolf ◽  
Sebastian Ruder
Keyword(s):  
Language Processing ◽  
Semantic Information ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Inductive Bias ◽  
Named Entity ◽  
Task Learning ◽  
Hidden States

Much effort has been devoted to evaluate whether multi-task learning can be leveraged to learn rich representations that can be used in various Natural Language Processing (NLP) down-stream applications. However, there is still a lack of understanding of the settings in which multi-task learning has a significant effect. In this work, we introduce a hierarchical model trained in a multi-task learning setup on a set of carefully selected semantic tasks. The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model. This model achieves state-of-the-art results on a number of tasks, namely Named Entity Recognition, Entity Mention Detection and Relation Extraction without hand-engineered features or external NLP tools like syntactic parsers. The hierarchical training supervision induces a set of shared semantic representations at lower layers of the model. We show that as we move from the bottom to the top layers of the model, the hidden states of the layers tend to represent more complex semantic information.

scholarly journals DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT

2020 ◽  
Vol 10 (18) ◽  
pp. 6429
Author(s):  
SungMin Yang ◽  
SoYeop Yoo ◽  
OkRan Jeong
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Language Model ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Knowledge Graph ◽  
Named Entity ◽  
Artificial Intelligence Technology

Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people’s language, in other words, natural language. For computers to learn on their own, the skill of understanding natural language is very important. There are a wide variety of tasks involved in the field of natural language processing, but we would like to focus on the named entity registration and relation extraction task, which is considered to be the most important in understanding sentences. We propose DeNERT-KG, a model that can extract subject, object, and relationships, to grasp the meaning inherent in a sentence. Based on the BERT language model and Deep Q-Network, the named entity recognition (NER) model for extracting subject and object is established, and a knowledge graph is applied for relation extraction. Using the DeNERT-KG model, it is possible to extract the subject, type of subject, object, type of object, and relationship from a sentence, and verify this model through experiments.

Location Named-Entity Recognition using Rule-Based Approach for Balinese Texts

2021 ◽  
Vol 9 (3) ◽  
pp. 435
Author(s):  
Ni Putu Ayu Sherly Anggita S ◽  
Ngurah Agus Sanjaya ER
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Main Task ◽  
Rule Based ◽  
Personal Names ◽  
Named Entity ◽  
Proposed Model ◽  
Rule Based Approach ◽  
F Measure

In Natural Language Processing (NLP), Named Recognition Entity (NER) is a sub-discussion widely used for research. The NER’s main task is to help identify and detect the entity-named in the sentence, such as personal names, locations, organizations, and many other entities. In this paper, we present a Location NER system for Balinese texts using a rule-based approach. NER in the Balinese document is an essential and challenging task because there is no research on this. The rule-based approach using human-made rules to extract entity name is one of the most famous ways to extract entity names as well as machine learning. The system aims to identify proper names in the corpus and classify them into locations class. Precision, recall, and F-measure used for the evaluation. Our results show that our proposed model is trustworthy enough, having average recall, precision, and f-measure values for the specific location entity, respectively, 0.935, 0.936, and 0.92. These results prove that our system is capable of recognizing named-entities of Balinese texts.

scholarly journals Integrated Model for Morphological Analysis and Named Entity Recognition Based on Label Attention Networks in Korean

2020 ◽  
Vol 10 (11) ◽  
pp. 3740
Author(s):  
Hongjin Kim ◽  
Harksoo Kim
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Error Propagation ◽  
Morphological Analysis ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Attention Networks ◽  
Named Entity ◽  
Proposed Model

In well-spaced Korean sentences, morphological analysis is the first step in natural language processing, in which a Korean sentence is segmented into a sequence of morphemes and the parts of speech of the segmented morphemes are determined. Named entity recognition is a natural language processing task carried out to obtain morpheme sequences with specific meanings, such as person, location, and organization names. Although morphological analysis and named entity recognition are closely associated with each other, they have been independently studied and have exhibited the inevitable error propagation problem. Hence, we propose an integrated model based on label attention networks that simultaneously performs morphological analysis and named entity recognition. The proposed model comprises two layers of neural network models that are closely associated with each other. The lower layer performs a morphological analysis, whereas the upper layer performs a named entity recognition. In our experiments using a public gold-labeled dataset, the proposed model outperformed previous state-of-the-art models used for morphological analysis and named entity recognition. Furthermore, the results indicated that the integrated architecture could alleviate the error propagation problem.

scholarly journals Deep learning with language models improves named entity recognition for PharmaCoNER

2021 ◽  
Vol 22 (S1) ◽  
Author(s):  
Cong Sun ◽  
Zhihao Yang ◽  
Lei Wang ◽  
Yin Zhang ◽  
Hongfei Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Domain Knowledge ◽  
Named Entity Recognition ◽  
Model Performance ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Language Models ◽  
Named Entity ◽  
Biomedical Texts

Abstract Background The recognition of pharmacological substances, compounds and proteins is essential for biomedical relation extraction, knowledge graph construction, drug discovery, as well as medical question answering. Although considerable efforts have been made to recognize biomedical entities in English texts, to date, only few limited attempts were made to recognize them from biomedical texts in other languages. PharmaCoNER is a named entity recognition challenge to recognize pharmacological entities from Spanish texts. Because there are currently abundant resources in the field of natural language processing, how to leverage these resources to the PharmaCoNER challenge is a meaningful study. Methods Inspired by the success of deep learning with language models, we compare and explore various representative BERT models to promote the development of the PharmaCoNER task. Results The experimental results show that deep learning with language models can effectively improve model performance on the PharmaCoNER dataset. Our method achieves state-of-the-art performance on the PharmaCoNER dataset, with a max F1-score of 92.01%. Conclusion For the BERT models on the PharmaCoNER dataset, biomedical domain knowledge has a greater impact on model performance than the native language (i.e., Spanish). The BERT models can obtain competitive performance by using WordPiece to alleviate the out of vocabulary limitation. The performance on the BERT model can be further improved by constructing a specific vocabulary based on domain knowledge. Moreover, the character case also has a certain impact on model performance.

scholarly journals ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

TEM Journal ◽  
2021 ◽  
pp. 82-94
Author(s):  
Maganti Syamala ◽  
N.J. Nalini
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Machine Learning Algorithms ◽  
Entity Recognition ◽  
Aspect Extraction ◽  
Named Entity ◽  
Measurement Analysis ◽  
Proposed Model ◽  
Research Problems ◽  
Rule Based Approach

Aspect based sentient analysis (ABSA) is identified as one of the current research problems in Natural Language Processing (NLP). Traditional ABSA requires manual aspect assignment for aspect extraction and sentiment analysis. In this paper, to automate the process, a domain-independent dynamic ABSA model by the fusion of Efficient Named Entity Recognition (E-NER) guided dependency parsing technique with Neural Networks (NN) is proposed. The extracted aspects and sentiment terms by E-NER are trained to a Convolutional Neural Network (CNN) using Word embedding’s technique. Aspect categorybased polarity prediction is evaluated using NLTK Vader Sentiment package. The proposed model was compared to traditional rule-based approach, and the proposed dynamic model proved to yield better results by 17% when validated in terms of correctly classified instances, accuracy, precision, recall and F-Score using machine learning algorithms.

scholarly journals Unsupervised cross-lingual model transfer for named entity recognition with contextualized word representations

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257230
Author(s):  
Huijiong Yan ◽  
Tao Qian ◽  
Liang Xie ◽  
Shanguang Chen
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Models ◽  
Competitive Performance ◽  
Named Entity ◽  
Model Transfer ◽  
The Cross ◽  
Cross Lingual

Named entity recognition (NER) is one fundamental task in the natural language processing (NLP) community. Supervised neural network models based on contextualized word representations can achieve highly-competitive performance, which requires a large-scale manually-annotated corpus for training. While for the resource-scarce languages, the construction of such as corpus is always expensive and time-consuming. Thus, unsupervised cross-lingual transfer is one good solution to address the problem. In this work, we investigate the unsupervised cross-lingual NER with model transfer based on contextualized word representations, which greatly advances the cross-lingual NER performance. We study several model transfer settings of the unsupervised cross-lingual NER, including (1) different types of the pretrained transformer-based language models as input, (2) the exploration strategies of the multilingual contextualized word representations, and (3) multi-source adaption. In particular, we propose an adapter-based word representation method combining with parameter generation network (PGN) better to capture the relationship between the source and target languages. We conduct experiments on a benchmark ConLL dataset involving four languages to simulate the cross-lingual setting. Results show that we can obtain highly-competitive performance by cross-lingual model transfer. In particular, our proposed adapter-based PGN model can lead to significant improvements for cross-lingual NER.

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