A novel deep learning architecture for drug named entity recognition

Drug named entity recognition (DNER) becomes the prerequisite of other medical relation extraction systems. Existing approaches to automatically recognize drug names includes rule-based, machine learning (ML) and deep learning (DL) techniques. DL techniques have been verified to be the state-of-the-art as it is independent of handcrafted features. The previous DL methods based on word embedding input representation uses the same vector representation for an entity irrespective of its context in different sentences and hence could not capture the context properly. Also, identification of the n-gram entity is a challenge. In this paper, a novel architecture is proposed that includes a sentence embedding layer that works on the entire sentence to efficiently capture the context of an entity. A hybrid model that comprises a stacked bidirectional long short-term memory (Bi-LSTM) with residual LSTM has been designed to overcome the limitations and to upgrade the performance of the model. We have contrasted the achievement of our proposed approach with other DNER models and the percentage of improvements of the proposed model over LSTM-conditional random field (CRF), LIU and WBI with respect to micro-average F1-score are 11.17, 8.8 and 17.64 respectively. The proposed model has also shown promising result in recognizing 2- and 3-gram entities

A Boundary Determined Neural Model for Relation Extraction

Existing models extract entity relations only after two entity spans have been precisely extracted that influenced the performance of relation extraction. Compared with recognizing entity spans, because the boundary has a small granularity and a less ambiguity, it can be detected precisely and incorporated to learn better representation. Motivated by the strengths of boundary, we propose a boundary determined neural (BDN) model, which leverages boundaries as task-related cues to predict the relation labels. Our model can predict high-quality relation instance via the pairs of boundaries, which can relieve error propagation problem. Moreover, our model fuses with boundary-relevant information encoding to represent distributed representation to improve the ability of capturing semantic and dependency information, which can increase the discriminability of neural network. Experiments show that our model achieves state-of-the-art performances on ACE05 corpus

Context-Aware Sentiment Analysis using Tweet Expansion Method

The large source of information space produced by the plethora of social media platforms in general and microblogging in particular has spawned a slew of new applications and prompted the rise and expansion of sentiment analysis research. We propose a sentiment analysis technique that identifies the main parts to describe tweet intent and also enriches them with relevant words, phrases, or even inferred variables. We followed a state-of-the-art hybrid deep learning model to combine Convolutional Neural Network (CNN) and the Long Short-Term Memory network (LSTM) to classify tweet data based on their polarity. To preserve the latent relationships between tweet terms and their expanded representation, sentence encoding and contextualized word embeddings are utilized. To investigate the performance of tweet embeddings on the sentiment analysis task, we tested several context-free models (Word2Vec, Sentence2Vec, Glove, and FastText), a dynamic embedding model (BERT), deep contextualized word representations (ELMo), and an entity-based model (Wikipedia). The proposed method and results prove that text enrichment improves the accuracy of sentiment polarity classification with a notable percentage

Context-Aware Sentiment Analysis using Tweet Expansion Method

The large source of information space produced by the plethora of social media platforms in general and microblogging in particular has spawned a slew of new applications and prompted the rise and expansion of sentiment analysis research. We propose a sentiment analysis technique that identifies the main parts to describe tweet intent and also enriches them with relevant words, phrases, or even inferred variables. We followed a state-of-the-art hybrid deep learning model to combine Convolutional Neural Network (CNN) and the Long Short-Term Memory network (LSTM) to classify tweet data based on their polarity. To preserve the latent relationships between tweet terms and their expanded representation, sentence encoding and contextualized word embeddings are utilized. To investigate the performance of tweet embeddings on the sentiment analysis task, we tested several context-free models (Word2Vec, Sentence2Vec, Glove, and FastText), a dynamic embedding model (BERT), deep contextualized word representations (ELMo), and an entity-based model (Wikipedia). The proposed method and results prove that text enrichment improves the accuracy of sentiment polarity classification with a notable percentage

TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tags

So far, discontinuous named entity recognition (NER) has received increasing research attention and many related methods have surged such as hypergraph-based methods, span-based methods, and sequence-to-sequence (Seq2Seq) methods, etc. However, these methods more or less suffer from some problems such as decoding ambiguity and efficiency, which limit their performance. Recently, grid-tagging methods, which benefit from the flexible design of tagging systems and model architectures, have shown superiority to adapt for various information extraction tasks. In this paper, we follow the line of such methods and propose a competitive grid-tagging model for discontinuous NER. We call our model TOE because we incorporate two kinds of Tag-Oriented Enhancement mechanisms into a state-of-the-art (SOTA) grid-tagging model that casts the NER problem into word-word relationship prediction. First, we design a Tag Representation Embedding Module (TREM) to force our model to consider not only word-word relationships but also word-tag and tag-tag relationships. Concretely, we construct tag representations and embed them into TREM, so that TREM can treat tag and word representations as queries/keys/values and utilize self-attention to model their relationships. On the other hand, motivated by the Next-Neighboring-Word (NNW) and Tail-Head-Word (THW) tags in the SOTA model, we add two new symmetric tags, namely Previous-Neighboring-Word (PNW) and Head-Tail-Word (HTW), to model more fine-grained word-word relationships and alleviate error propagation from tag prediction. In the experiments of three benchmark datasets, namely CADEC, ShARe13 and ShARe14, our TOE model pushes the SOTA results by about 0.83%, 0.05% and 0.66% in F1, demonstrating its effectiveness

A Bi-Directional GRU Architecture for the Self-Attention Mechanism: An Adaptable, Multi-Layered Approach with Blend of Word Embedding

Sentiment analysis (SA) has become an essential component of natural language processing (NLP) with numerous practical applications to understanding “what other people think”. Various techniques have been developed to tackle SA using deep learning (DL); however, current research lacks comprehensive strategies incorporating multiple-word embeddings. This study proposes a self-attention mechanism that leverages DL and involves the contextual integration of word embedding with a time-dispersed bidirectional gated recurrent unit (Bi-GRU). This work employs word embedding approaches GloVe, word2vec, and fastText to achieve better predictive capabilities. By integrating these techniques, the study aims to improve the classifier’s capability to precisely analyze and categorize sentiments in textual data from the domain of movies. The investigation seeks to enhance the classifier’s performance in NLP tasks by addressing the challenges of underfitting and overfitting in DL. To evaluate the model’s effectiveness, an openly available IMDb dataset was utilized, achieving a remarkable testing accuracy of 99.70%

Enhance Representation Learning of Clinical Narrative with Neural Networks for Clinical Predictive Modeling

Medicine is undergoing a technological revolution. Understanding human health from clinical data has major challenges from technical and practical perspectives, thus prompting methods that understand large, complex, and noisy data. These methods are particularly necessary for natural language data from clinical narratives/notes, which contain some of the richest information on a patient. Meanwhile, deep neural networks have achieved superior performance in a wide variety of natural language processing (NLP) tasks because of their capacity to encode meaningful but abstract representations and learn the entire task end-to-end. In this thesis, I investigate representation learning of clinical narratives with deep neural networks through a number of tasks ranging from clinical concept extraction, clinical note modeling, and patient-level language representation. I present methods utilizing representation learning with neural networks to support understanding of clinical text documents. I first introduce the notion of representation learning from natural language processing and patient data modeling. Then, I investigate word-level representation learning to improve clinical concept extraction from clinical notes. I present two works on learning word representations and evaluate them to extract important concepts from clinical notes. The first study focuses on cancer-related information, and the second study evaluates shared-task data. The aims of these two studies are to automatically extract important entities from clinical notes. Next, I present a series of deep neural networks to encode hierarchical, longitudinal, and contextual information for modeling a series of clinical notes. I also evaluate the models by predicting clinical outcomes of interest, including mortality, length of stay, and phenotype predictions. Finally, I propose a novel representation learning architecture to develop a generalized and transferable language representation at the patient level. I also identify pre-training tasks appropriate for constructing a generalizable language representation. The main focus is to improve predictive performance of phenotypes with limited data, a challenging task due to a lack of data. Overall, this dissertation addresses issues in natural language processing for medicine, including clinical text classification and modeling. These studies show major barriers to understanding large-scale clinical notes. It is believed that developing deep representation learning methods for distilling enormous amounts of heterogeneous data into patient-level language representations will improve evidence-based clinical understanding. The approach to solving these issues by learning representations could be used across clinical applications despite noisy data. I conclude that considering different linguistic components in natural language and sequential information between clinical events is important. Such results have implications beyond the immediate context of predictions and further suggest future directions for clinical machine learning research to improve clinical outcomes. This could be a starting point for future phenotyping methods based on natural language processing that construct patient-level language representations to improve clinical predictions. While significant progress has been made, many open questions remain, so I will highlight a few works to demonstrate promising directions