scholarly journals Biomedical Concept Recognition Using Deep Neural Sequence Models

2019 ◽  
Author(s):  
Negacy D. Hailu ◽  
Michael Bada ◽  
Asmelash Teka Hadgu ◽  
Lawrence E. Hunter
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Concept Recognition ◽  
Performance Improvements ◽  
Art Performance

AbstractBackgroundthe automated identification of mentions of ontological concepts in natural language texts is a central task in biomedical information extraction. Despite more than a decade of effort, performance in this task remains below the level necessary for many applications.Resultsrecently, applications of deep learning in natural language processing have demonstrated striking improvements over previously state-of-the-art performance in many related natural language processing tasks. Here we demonstrate similarly striking performance improvements in recognizing biomedical ontology concepts in full text journal articles using deep learning techniques originally developed for machine translation. For example, our best performing system improves the performance of the previous state-of-the-art in recognizing terms in the Gene Ontology Biological Process hierarchy, from a previous best F1 score of 0.40 to an F1 of 0.70, nearly halving the error rate. Nearly all other ontologies show similar performance improvements.ConclusionsA two-stage concept recognition system, which is a conditional random field model for span detection followed by a deep neural sequence model for normalization, improves the state-of-the-art performance for biomedical concept recognition. Treating the biomedical concept normalization task as a sequence-to-sequence mapping task similar to neural machine translation improves performance.

scholarly journals Text: An R-package for Analyzing and Visualizing Human Language Using Natural Language Processing and Deep Learning

2021 ◽  
Author(s):  
Oscar Nils Erik Kjell ◽  
H. Andrew Schwartz ◽  
Salvatore Giorgi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Rating Scale ◽  
State Of The Art ◽  
R Package ◽  
Language Models ◽  
Categorical Variables ◽  
Human Language

The language that individuals use for expressing themselves contains rich psychological information. Recent significant advances in Natural Language Processing (NLP) and Deep Learning (DL), namely transformers, have resulted in large performance gains in tasks related to understanding natural language such as machine translation. However, these state-of-the-art methods have not yet been made easily accessible for psychology researchers, nor designed to be optimal for human-level analyses. This tutorial introduces text (www.r-text.org), a new R-package for analyzing and visualizing human language using transformers, the latest techniques from NLP and DL. Text is both a modular solution for accessing state-of-the-art language models and an end-to-end solution catered for human-level analyses. Hence, text provides user-friendly functions tailored to test hypotheses in social sciences for both relatively small and large datasets. This tutorial describes useful methods for analyzing text, providing functions with reliable defaults that can be used off-the-shelf as well as providing a framework for the advanced users to build on for novel techniques and analysis pipelines. The reader learns about six methods: 1) textEmbed: to transform text to traditional or modern transformer-based word embeddings (i.e., numeric representations of words); 2) textTrain: to examine the relationships between text and numeric/categorical variables; 3) textSimilarity and 4) textSimilarityTest: to computing semantic similarity scores between texts and significance test the difference in meaning between two sets of texts; and 5) textProjection and 6) textProjectionPlot: to examine and visualize text within the embedding space according to latent or specified construct dimensions (e.g., low to high rating scale scores).

scholarly journals Analyzing Architectures for Neural Machine Translation using Low Computational Resources

2021 ◽  
Vol 10 (5) ◽  
pp. 9-16
Author(s):  
Aditya Mandke ◽  
Onkar Litake ◽  
Dipali Kadam
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
State Of The Art ◽  
Time Constraints ◽  
Neural Machine Translation ◽  
Recent Developments ◽  
Computationally Expensive ◽  
Computational Resources

With the recent developments in the field of Natural Language Processing, there has been a rise in the use of different architectures for Neural Machine Translation. Transformer architectures are used to achieve state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such setups consisting of high-end GPUs and other resources. We train our models on low computational resources and investigate the results. As expected, transformers outperformed other architectures, but there were some surprising results. Transformers consisting of more encoders and decoders took more time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively less time to train than transformers, making it suitable to use in situations having time constraints.

scholarly journals A SYSTEMATIC READING IN STATISTICAL TRANSLATION: FROM THE STATISTICAL MACHINE TRANSLATION TO THE NEURAL TRANSLATION MODELS.

2017 ◽  
Author(s):  
Zakaria El Maazouzi ◽  
Badr Eddine EL Mohajir ◽  
Mohammed Al Achhab
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
High Accuracy ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Automatic Translation

Achieving high accuracy in automatic translation tasks has been one of the challenging goals for researchers in the area of machine translation since decades. Thus, the eagerness of exploring new possible ways to improve machine translation was always the matter for researchers in the field. Automatic translation as a key application in the natural language processing domain has developed many approaches, namely statistical machine translation and recently neural machine translation that improved largely the translation quality especially for Latin languages. They have even made it possible for the translation of some language pairs to approach human translation quality. In this paper, we present a survey of the state of the art of statistical translation, where we describe the different existing methodologies, and we overview the recent research studies while pointing out the main strengths and limitations of the different approaches.  

scholarly journals Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts

2017 ◽  
Vol 24 (4) ◽  
pp. 813-821 ◽  
Author(s):  
Anne Cocos ◽  
Alexander G Fiks ◽  
Aaron J Masino
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
State Of The Art ◽  
Detection Performance ◽  
Word Embeddings

Abstract Objective Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media. Materials and Methods We developed a recurrent neural network (RNN) model that labels words in an input sequence with ADR membership tags. The only input features are word-embedding vectors, which can be formed through task-independent pretraining or during ADR detection training. Results Our best-performing RNN model used pretrained word embeddings created from a large, non–domain-specific Twitter dataset. It achieved an approximate match F-measure of 0.755 for ADR identification on the dataset, compared to 0.631 for a baseline lexicon system and 0.65 for the state-of-the-art conditional random field model. Feature analysis indicated that semantic information in pretrained word embeddings boosted sensitivity and, combined with contextual awareness captured in the RNN, precision. Discussion Our model required no task-specific feature engineering, suggesting generalizability to additional sequence-labeling tasks. Learning curve analysis showed that our model reached optimal performance with fewer training examples than the other models. Conclusions ADR detection performance in social media is significantly improved by using a contextually aware model and word embeddings formed from large, unlabeled datasets. The approach reduces manual data-labeling requirements and is scalable to large social media datasets.

scholarly journals Enriching Word Vectors with Subword Information

2017 ◽  
Vol 5 ◽  
pp. 135-146 ◽  
Author(s):  
Piotr Bojanowski ◽  
Edouard Grave ◽  
Armand Joulin ◽  
Tomas Mikolov
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Training Data ◽  
Vector Representation ◽  
New Approach ◽  
Word Similarity ◽  
Art Performance ◽  
N Gram

Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows us to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.

scholarly journals Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling

2018 ◽  
Author(s):  
Tao Shen ◽  
Tianyi Zhou ◽  
Guodong Long ◽  
Jing Jiang ◽  
Sen Wang ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Mutual Benefit ◽  
Fusion Model ◽  
Attention Network ◽  
Sequence Modeling ◽  
Policy Gradient ◽  
Art Performance

Many natural language processing tasks solely rely on sparse dependencies between a few tokens in a sentence. Soft attention mechanisms show promising performance in modeling local/global dependencies by soft probabilities between every two tokens, but they are not effective and efficient when applied to long sentences. By contrast, hard attention mechanisms directly select a subset of tokens but are difficult and inefficient to train due to their combinatorial nature. In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other. In ReSA, a hard attention trims a sequence for a soft self-attention to process, while the soft attention feeds reward signals back to facilitate the training of the hard one. For this purpose, we develop a novel hard attention called "reinforced sequence sampling (RSS)", selecting tokens in parallel and trained via policy gradient. Using two RSS modules, ReSA efficiently extracts the sparse dependencies between each pair of selected tokens. We finally propose an RNN/CNN-free sentence-encoding model, "reinforced self-attention network (ReSAN)", solely based on ReSA.  It achieves state-of-the-art performance on both the Stanford Natural Language Inference (SNLI) and the Sentences Involving Compositional Knowledge (SICK) datasets. 

scholarly journals Evaluating Multilingual BERT for Estonian

2020 ◽  
Author(s):  
Claudia Kittask ◽  
Kirill Milintsevich ◽  
Kairit Sirts
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Classification ◽  
State Of The Art ◽  
Language Models ◽  
Neural Models ◽  
Comparable Level ◽  
Art Performance ◽  
Multiple Languages

Recently, large pre-trained language models, such as BERT, have reached state-of-the-art performance in many natural language processing tasks, but for many languages, including Estonian, BERT models are not yet available. However, there exist several multilingual BERT models that can handle multiple languages simultaneously and that have been trained also on Estonian data. In this paper, we evaluate four multilingual models—multilingual BERT, multilingual distilled BERT, XLM and XLM-RoBERTa—on several NLP tasks including POS and morphological tagging, NER and text classification. Our aim is to establish a comparison between these multilingual BERT models and the existing baseline neural models for these tasks. Our results show that multilingual BERT models can generalise well on different Estonian NLP tasks outperforming all baselines models for POS and morphological tagging and text classification, and reaching the comparable level with the best baseline for NER, with XLM-RoBERTa achieving the highest results compared with other multilingual models.

Deep Learning

2018 ◽  
Author(s):  
Kyunghyun Cho
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Computational Linguistics ◽  
Language Processing ◽  
Document Classification ◽  
Context Modelling ◽  
Language Modelling

Deep learning has rapidly gained huge popularity among researchers in natural-language processing and computational linguistics in recent years. This chapter gives a comprehensive and detailed overview of recent deep-learning-based approaches to challenging problems in natural-language processing, specifically focusing on document classification, language modelling, and machine translation. At the end of the chapter, new opportunities in natural-language processing made possible by deep learning are discussed, which are multilingual and larger-context modelling.

scholarly journals Box: Natural Language Processing Research Using Amazon Web Services

2015 ◽  
Vol 104 (1) ◽  
pp. 27-38
Author(s):  
Amittai Axelrod
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Machine Translation ◽  
Language Processing ◽  
State Of The Art ◽  
Research Community ◽  
Development Platform ◽  
Amazon Web Services ◽  
Art Research

Abstract We present a publicly-available state-of-the-art research and development platform for Machine Translation and Natural Language Processing that runs on the Amazon Elastic Compute Cloud. This provides a standardized research environment for all users, and enables perfect reproducibility and compatibility. Box also enables users to use their hardware budget to avoid the management and logistical overhead of maintaining a research lab, yet still participate in global research community with the same state-of-the-art tools.

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