Named Entity Recognition in the Medical Domain with Constrained CRF Models

Abstract

This paper investigates how to improve performance on information extraction tasks by constraining and sequencing CRF-based approaches. We consider two different relation extraction tasks, both from the medical literature: dependence relations and probability statements. We explore whether adding constraints can lead to an improvement over standard CRF decoding. Results on our relation extraction tasks are promising, showing significant increases in performance from both (i) adding constraints to post-process the output of a baseline CRF, which captures "domain knowledge", and (ii) further allowing flexibility in the application of those constraints by leveraging a binary classifier as a pre-processing step.

“Opinion Target: Opinion Word” Pairs Extraction Based on CRF

“…(1) Frequency-based methods [1,2]; (2) Rule/template-based methods [3,4]; (3) Graph theory-based methods [6][7][8]; (4) Based on CRF or combined CRF and deep learning methods [5,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

“…The frequency-based method is quite simple and has a high recognition rate for opinion targets with high frequency, but it is easy for it to increase noise and miss opinion targets with low frequency [1]. While the method based on rules/templates has high extraction accuracy, it is limited to a specific field, resulting in the poor generality of the model [5,9]. Methods based on graph theory often assume that the opinion targets are nouns, nominal phrases, adjectives or adjective phrases, which makes it very difficult for the method to recognize targets that do not correspond to those types.…”

“…The traditional methods for opinion target extraction are based on frequency or manually designed rules [1][2][3][4], and it is not difficult to find obvious disadvantages, such as the fact that they are easily influenced by noise and have low flexibility, etc. With the development of machine learning theory, increasing numbers of researchers would like to choose a CRF-based model [5] to complete the same task. Current research into opinion target extraction is limited to indicators such as accuracy, without extracting them and making pairs.…”

An Instance Transfer-Based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition

“…However it needs to pass through the corpus to build a feature vector corresponding to all the contexts in one document. Moreover, a large number of researches about domain NER are on biomedical fields (Murugesan et al, 2017;Crichton et al, 2017), and the typical methods are CRF (Seker and Eryigit, 2017;Jochim and Deleris, 2017), and other supervised learning approaches (Jain, 2015).…”

Multifeature Named Entity Recognition in Information Security Based on Adversarial Learning

“…Named entity recognition (NER) aims to extract various types of entities from text. This is a fundamental step in text mining and has received much attention recently, especially in medicine [1][2][3][4][5][6] and biochemistry [7][8][9][10]. In contrast, the development of NER tasks in information security has been relatively slow.…”