Better Feature Integration for Named Entity Recognition (original) (raw)

Dependency-Guided LSTM-CRF for Named Entity Recognition

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019

Dependency tree structures capture longdistance and syntactic relationships between words in a sentence. The syntactic relations (e.g., nominal subject, object) can potentially infer the existence of certain named entities. In addition, the performance of a named entity recognizer could benefit from the longdistance dependencies between the words in dependency trees. In this work, we propose a simple yet effective dependency-guided LSTM-CRF model to encode the complete dependency trees and capture the above properties for the task of named entity recognition (NER). The data statistics show strong correlations between the entity types and dependency relations. We conduct extensive experiments on several standard datasets and demonstrate the effectiveness of the proposed model in improving NER and achieving state-of-theart performance. Our analysis reveals that the significant improvements mainly result from the dependency relations and long-distance interactions provided by dependency trees.

FlexNER: A Flexible LSTM-CNN Stack Framework for Named Entity Recognition

Natural Language Processing and Chinese Computing, 2019

Named entity recognition (NER) is a foundational technology for information extraction. This paper presents a flexible NER framework 6 compatible with different languages and domains. Inspired by the idea of distant supervision (DS), this paper enhances the representation by increasing the entity-context diversity without relying on external resources. We choose different layer stacks and sub-network combinations to construct the bilateral networks. This strategy can generally improve model performance on different datasets. We conduct experiments on five languages, such as English, German, Spanish, Dutch and Chinese, and biomedical fields, such as identifying the chemicals and gene/protein terms from scientific works. Experimental results demonstrate the good performance of this framework.

A Survey on Recent Advances in Named Entity Recognition from Deep Learning models

ArXiv, 2018

Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.

A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition

2019

We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-ofspeech (POS) tags, and morphological information. While previous work has focused on learning from raw word input, using word and character embeddings only, we show that for morphologically rich languages, such as Bulgarian, access to POS information contributes more to the performance gains than the detailed morphological information. Thus, we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity. Our evaluation results over a standard dataset show sizeable improvements over the state-of-the-art for Bulgarian NER.

LSTM-CRF Models for Named Entity Recognition

IEICE Transactions on Information and Systems

Recurrent neural networks (RNNs) are a powerful model for sequential data. RNNs that use long short-term memory (LSTM) cells have proven effective in handwriting recognition, language modeling, speech recognition, and language comprehension tasks. In this study, we propose LSTM conditional random fields (LSTM-CRF); it is an LSTMbased RNN model that uses output-label dependencies with transition features and a CRF-like sequence-level objective function. We also propose variations to the LSTM-CRF model using a gate recurrent unit (GRU) and structurally constrained recurrent network (SCRN). Empirical results reveal that our proposed models attain state-of-the-art performance for named entity recognition.

CharNER: Character-Level Named Entity Recognition

We describe and evaluate a character-level tagger for language-independent Named Entity Recognition (NER). Instead of words, a sentence is represented as a sequence of characters. The model consists of stacked bidirectional LSTMs which inputs characters and outputs tag probabilities for each character. These probabilities are then converted to consistent word level named entity tags using a Viterbi decoder. We are able to achieve close to state-of-the-art NER performance in seven languages with the same basic model using only labeled NER data and no hand-engineered features or other external resources like syntactic taggers or Gazetteers.

Deep Exhaustive Model for Nested Named Entity Recognition

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a simple deep neural model for nested named entity recognition (NER). Most NER models focused on flat entities and ignored nested entities, which failed to fully capture underlying semantic information in texts.

Improving Neural Named Entity Recognition with Gazetteers

arXiv, 2020

The goal of this work is to improve the performance of a neu-ral named entity recognition system by adding input features that indicate a word is part of a name included in a gazetteer. This article describes how to generate gazetteers from the Wikidata knowledge graph as well as how to integrate the information into a neural NER system. Experiments reveal that the approach yields performance gains in two distinct languages: a high-resource, word-based language, English and a high-resource, character-based language, Chinese. Experiments were also performed in a low-resource language, Rus-sian on a newly annotated Russian NER corpus from Reddit tagged with four core types and twelve extended types. This article reports a baseline score. It is a longer version of a paper in the 33rd FLAIRS conference (Song et al. 2020).

Named Entity Recognition with Stack Residual LSTM and Trainable Bias Decoding

2017

Recurrent Neural Network models are the state-of-the-art for Named Entity Recognition (NER). We present two innovations to improve the performance of these models. The first innovation is the introduction of residual connections between the Stacked Recurrent Neural Network model to address the degradation problem of deep neural networks. The second innovation is a bias decoding mechanism that allows the trained system to adapt to non-differentiable and externally computed objectives, such as the entity-based F-measure. Our work improves the state-of-the-art results for both Spanish and English languages on the standard train/development/test split of the CoNLL 2003 Shared Task NER dataset.

A Survey On Deep Learning Approaches For Named Entity Recognition

2022

Named Entity Recognition (NER) is considered as a task of Information Extraction (IE) which is effective for improving the efficiency of a variety of Natural Language Processing (NLP) tasks, including Relation Extraction (RE), Question Answering (QA), Information Retrieval (IR), etc.NER tries to identify and classify named entities from a specified text, like persons, locations, and organizations, etc. Many researchers have discussed this problem through a variety of approaches, including rule-based and machine learning-based approaches. In recent years, many NER approaches that are based on deep learning have been proposed and improved to obtain precise results .In this paper, a survey of deep learning approaches for NER was presented. Also, the datasets and the evaluation metrics used in each approach were demonstrated. Then, a discussion was provided about the surveyed articles in terms of deep learning NER approaches together with the datasets and evaluation metrics used with ea...