Towards corpus and model: Hierarchical structured-attention-based features for Indonesian named entity recognition

2021 ◽  
pp. 1-12
Author(s):  
Yingwen Fu ◽  
Nankai Lin ◽  
Xiaotian Lin ◽  
Shengyi Jiang
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Models ◽  
Neural Models ◽  
Performance Models ◽  
Named Entity ◽  
High Resource ◽  
Benchmark Datasets

Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets.

Named Entity Recognition and Relation Extraction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-39
Author(s):  
Zara Nasar ◽  
Syed Waqar Jaffry ◽  
Muhammad Kamran Malik
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
The State ◽  
Entity Recognition ◽  
Joint Models ◽  
Named Entity ◽  
Textual Data ◽  
Benchmark Datasets

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

scholarly journals Recursively Binary Modification Model for Nested Named Entity Recognition

2020 ◽  
Vol 34 (05) ◽  
pp. 8164-8171
Author(s):  
Bing Li ◽  
Shifeng Liu ◽  
Yifang Sun ◽  
Wei Wang ◽  
Xiang Zhao
Keyword(s):  
Strong Evidence ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Bayesian Framework ◽  
Entity Recognition ◽  
Named Entities ◽  
Named Entity ◽  
Nested Structures ◽  
Benchmark Datasets ◽  
Head Component

Recently, there has been an increasing interest in identifying named entities with nested structures. Existing models only make independent typing decisions on the entire entity span while ignoring strong modification relations between sub-entity types. In this paper, we present a novel Recursively Binary Modification model for nested named entity recognition. Our model utilizes the modification relations among sub-entities types to infer the head component on top of a Bayesian framework and uses entity head as a strong evidence to determine the type of the entity span. The process is recursive, allowing lower-level entities to help better model those on the outer-level. To the best of our knowledge, our work is the first effort that uses modification relation in nested NER task. Extensive experiments on four benchmark datasets demonstrate that our model outperforms state-of-the-art models in nested NER tasks, and delivers competitive results with state-of-the-art models in flat NER task, without relying on any extra annotations or NLP tools.

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.

Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering

2021 ◽  
Vol 2 (4) ◽  
pp. 1-24
Author(s):  
Pratyay Banerjee ◽  
Kuntal Kumar Pal ◽  
Murthy Devarakonda ◽  
Chitta Baral
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Neural Models ◽  
Named Entity ◽  
Input Text ◽  
The Impact ◽  
Biomedical Named Entity Recognition ◽  
Entity Class

In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e., B , I , O only) compared to multiple entity-classes in traditional NER (i.e., B entity 1, B entity 2, I entity 1, I , O ), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.

scholarly journals Large Language Models for Latvian Named Entity Recognition

2020 ◽  
Author(s):  
Rinalds Vīksna ◽  
Inguna Skadiņa
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Models ◽  
Named Entity ◽  
Language Data

Transformer-based language models pre-trained on large corpora have demonstrated good results on multiple natural language processing tasks for widely used languages including named entity recognition (NER). In this paper, we investigate the role of the BERT models in the NER task for Latvian. We introduce the BERT model pre-trained on the Latvian language data. We demonstrate that the Latvian BERT model, pre-trained on large Latvian corpora, achieves better results (81.91 F1-measure on average vs 78.37 on M-BERT for a dataset with nine named entity types, and 79.72 vs 78.83 on another dataset with seven types) than multilingual BERT and outperforms previously developed Latvian NER systems.

Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer

2018 ◽  
Author(s):  
Xiaocheng Feng ◽  
Xiachong Feng ◽  
Bing Qin ◽  
Zhangyin Feng ◽  
Ting Liu
Keyword(s):  
Neural Networks ◽  
Knowledge Transfer ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Semantic Representations ◽  
Low Resource ◽  
Named Entity ◽  
High Resource ◽  
Distribution Features

Neural networks have been widely used for high resource language (e.g. English) named entity recognition (NER) and have shown state-of-the-art results.However, for low resource languages, such as Dutch, Spanish, due to the limitation of resources and lack of annotated data, taggers tend to have lower performances.To narrow this gap, we propose three novel strategies to enrich the semantic representations of low resource languages: we first develop neural networks to improve low resource word representations by knowledge transfer from high resource language using bilingual lexicons. Further, a lexicon extension strategy is designed to address out-of lexicon problem by automatically learning semantic projections.Thirdly, we regard word-level entity type distribution features as an external language-independent knowledge and incorporate them into our neural architecture. Experiments on two low resource languages (including Dutch and Spanish) demonstrate the effectiveness of these additional semantic representations (average 4.8\% improvement). Moreover, on Chinese OntoNotes 4.0 dataset, our approach achieved an F-score of 83.07\% with 2.91\% absolute gain compared to the state-of-the-art results.

scholarly journals Domain-Transferable Method for Named Entity Recognition Task

2020 ◽  
Author(s):  
Vladislav Mikhailov ◽  
Tatiana Shavrina
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
Neural Models ◽  
Named Entities ◽  
Named Entity ◽  
Domain Specific ◽  
Human Effort

Named Entity Recognition (NER) is a fundamental task in the fields of natural language processing and information extraction. NER has been widely used as a standalone tool or an essential component in a variety of applications such as question answering, dialogue assistants and knowledge graphs development. However, training reliable NER models requires a large amount of labelled data which is expensive to obtain, particularly in specialized domains. This paper describes a method to learn a domain-specific NER model for an arbitrary set of named entities when domain-specific supervision is not available. We assume that the supervision can be obtained with no human effort, and neural models can learn from each other. The code, data and models are publicly available.

scholarly journals A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings

2020 ◽  
Vol 34 (05) ◽  
pp. 9090-9097
Author(s):  
Niels Van der Heijden ◽  
Samira Abnar ◽  
Ekaterina Shutova
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Word Embeddings ◽  
Named Entity ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Joint Training ◽  
Comprehensive Comparison

The lack of annotated data in many languages is a well-known challenge within the field of multilingual natural language processing (NLP). Therefore, many recent studies focus on zero-shot transfer learning and joint training across languages to overcome data scarcity for low-resource languages. In this work we (i) perform a comprehensive comparison of state-of-the-art multilingual word and sentence encoders on the tasks of named entity recognition (NER) and part of speech (POS) tagging; and (ii) propose a new method for creating multilingual contextualized word embeddings, compare it to multiple baselines and show that it performs at or above state-of-the-art level in zero-shot transfer settings. Finally, we show that our method allows for better knowledge sharing across languages in a joint training setting.

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 Transfer Learning for Named Entity Recognition in Financial and Biomedical Documents

Information ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 248 ◽  
Author(s):  
Sumam Francis ◽  
Jordy Van Landeghem ◽  
Marie-Francine Moens
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Training Data ◽  
Entity Recognition ◽  
Language Models ◽  
Reasonable Assumption ◽  
Target Domain ◽  
Named Entity

Recent deep learning approaches have shown promising results for named entity recognition (NER). A reasonable assumption for training robust deep learning models is that a sufficient amount of high-quality annotated training data is available. However, in many real-world scenarios, labeled training data is scarcely present. In this paper we consider two use cases: generic entity extraction from financial and from biomedical documents. First, we have developed a character based model for NER in financial documents and a word and character based model with attention for NER in biomedical documents. Further, we have analyzed how transfer learning addresses the problem of limited training data in a target domain. We demonstrate through experiments that NER models trained on labeled data from a source domain can be used as base models and then be fine-tuned with few labeled data for recognition of different named entity classes in a target domain. We also witness an interest in language models to improve NER as a way of coping with limited labeled data. The current most successful language model is BERT. Because of its success in state-of-the-art models we integrate representations based on BERT in our biomedical NER model along with word and character information. The results are compared with a state-of-the-art model applied on a benchmarking biomedical corpus.

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