Biomedical even trigger identification based on the gated unit neural network and word representation

2021 ◽  
Author(s):  
Sitong Liu ◽  
Sheng Sun ◽  
Houjun Tang
Keyword(s):  
Neural Network ◽  
Word Representation

scholarly journals Natural language description of images using hybrid recurrent neural network

2019 ◽  
Vol 9 (4) ◽  
pp. 2932
Author(s):  
Md. Asifuzzaman Jishan ◽  
Khan Raqib Mahmud ◽  
Abul Kalam Al Azad
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Text Line ◽  
Short Term ◽  
Word Representation ◽  
Benchmark Datasets ◽  
Long Short Term Memory ◽  
Language Description

We presented a learning model that generated natural language description of images. The model utilized the connections between natural language and visual data by produced text line based contents from a given image. Our Hybrid Recurrent Neural Network model is based on the intricacies of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional Recurrent Neural Network (BRNN) models. We conducted experiments on three benchmark datasets, e.g., Flickr8K, Flickr30K, and MS COCO. Our hybrid model utilized LSTM model to encode text line or sentences independent of the object location and BRNN for word representation, this reduced the computational complexities without compromising the accuracy of the descriptor. The model produced better accuracy in retrieving natural language based description on the dataset.

Attention-Based Multimodal Neural Network for Automatic Evaluation of Press Conferences

2020 ◽  
Vol 11 (3) ◽  
pp. 1-19
Author(s):  
Shengzhou Yi ◽  
Koshiro Mochitomi ◽  
Isao Suzuki ◽  
Xueting Wang ◽  
Toshihiko Yamasaki
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Language Model ◽  
Evaluation Criteria ◽  
Attention Network ◽  
Audio Features ◽  
Average Accuracy ◽  
Word Representation ◽  
Audio Data ◽  
Text Features

In the study, a multimodal neural network is proposed to automatically predict the evaluation of a professional consultant team for press conferences using text and audio data. Seven publicly available press conference videos were collected, and all the Q&A pairs between speakers and journalists were annotated by the consultant team. The proposed multimodal neural network consists of a language model, an audio model, and a feature fusion network. The word representation is made up by a token embedding using ELMo and a type embedding. The language model is an LSTM with an attention layer. The audio model is based on a six-layer CNN to extract segmental feature as well as an attention network to measure the importance of each segment. Two approaches of feature fusion are proposed: a shared attention network and the production of text features and audio features. The former can explain the importance between speech content and speaking style. The latter achieved the best performance with the average accuracy of 60.1% for all evaluation criteria.

scholarly journals ColNet: Embedding the Semantics of Web Tables for Column Type Prediction

2019 ◽  
Vol 33 ◽  
pp. 29-36 ◽  
Author(s):  
Jiaoyan Chen ◽  
Ernesto Jiménez-Ruiz ◽  
Ian Horrocks ◽  
Charles Sutton
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
State Of The Art ◽  
Column Type ◽  
Basic Understanding ◽  
Word Representation ◽  
Meta Information ◽  
A Cell ◽  
Multiple Cells

Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables. Current methods rely on either table metadata like column name or entity correspondences of cells in the KB, and may fail to deal with growing web tables with incomplete meta information. In this paper we propose a neural network based column type annotation framework named ColNet which is able to integrate KB reasoning and lookup with machine learning and can automatically train Convolutional Neural Networks for prediction. The prediction model not only considers the contextual semantics within a cell using word representation, but also embeds the semantics of a column by learning locality features from multiple cells. The method is evaluated with DBPedia and two different web table datasets, T2Dv2 from the general Web and Limaye from Wikipedia pages, and achieves higher performance than the state-of-the-art approaches.

scholarly journals Semantic Web Mining for Analyzing Retail Environment Using Word2Vec and CNN-FK

2021 ◽  
Vol 26 (3) ◽  
pp. 311-318
Author(s):  
Praveen Kumar Yechuri ◽  
Suguna Ramadass
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Semantic Web ◽  
Web Mining ◽  
Convolution Neural Network ◽  
It Services ◽  
Retail Environment ◽  
Word Representation ◽  
Function Diagram ◽  
Work Done

Digital Technology is becoming increasingly essential to organizations. Related knowledge is important for a company to allow optimal use of its IT services. The use of Big Data is relatively new to this field. Handling Big data is not, at this stage, a problem for large business organizations in particular; it has also become a challenge for small and medium-sized businesses. Although Semantic Web analysis is largely focused on fundamental advances that are expected to make the Semantic Web a reality, there has not been much work done to demonstrate the feasibility and effect of the Semantic Web on business issues. The infrastructure of electronic information executives and business types has provided various enhancements for companies, such as the automated process of buying and selling products. Nevertheless, undertakings are checked for the multifaceted nature of the extension required to deal with an ever-increasing number of electronic details and procedures. This paper suggests a model with a neural network design and a word representation system named Word2Vec for analyzing retail environment. Firstly, Word2vec manages the text data and shows it as a function diagram and a feature map is given to the Convolution Neural Network (CNN) that extracts the features and classifies them. The IMDB dataset, the Cornell dataset, the Amazon Products Dataset and the Twitter dataset were analyzed in the proposed model. The proposed Convolution Neural Network Fisher Kernel (CNN-FK) model is compared with the existing SVM model for analyzing retail environment in semantic web mining. The new approach has increased efficiency when compared to existing models.

scholarly journals Using Factored Word Representation in Neural Network Language Models

2016 ◽  
Author(s):  
Jan Niehues ◽  
Thanh-Le Ha ◽  
Eunah Cho ◽  
Alex Waibel
Keyword(s):  
Neural Network ◽  
Language Models ◽  
Word Representation ◽  
Network Language

scholarly journals Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings

2019 ◽  
Vol 27 (1) ◽  
pp. 47-55 ◽  
Author(s):  
Hong-Jie Dai ◽  
Chu-Hsien Su ◽  
Chi-Shin Wu
Keyword(s):  
Neural Network ◽  
Adverse Drug Event ◽  
Conditional Random Fields ◽  
Principal Component ◽  
Medical Intervention ◽  
Drug Event ◽  
Word Embeddings ◽  
Word Representation ◽  
Clinical Records ◽  
Medical Concepts

Abstract Objective An adverse drug event (ADE) refers to an injury resulting from medical intervention related to a drug including harm caused by drugs or from the usage of drugs. Extracting ADEs from clinical records can help physicians associate adverse events to targeted drugs. Materials and Methods We proposed a cascading architecture to recognize medical concepts including ADEs, drug names, and entities related to drugs. The architecture includes a preprocessing method and an ensemble of conditional random fields (CRFs) and neural network–based models to respectively address the challenges of surrogate string and overlapping annotation boundaries observed in the employed ADEs and medication extraction (ADME) corpus. The effectiveness of applying different pretrained and postprocessed word embeddings for the ADME task was also studied. Results The empirical results showed that both CRFs and neural network–based models provide promising solution for the ADME task. The neural network–based models particularly outperformed CRFs in concept types involving narrative descriptions. Our best run achieved an overall micro F-score of 0.919 on the employed corpus. Our results also suggested that the Global Vectors for word representation embedding in general domain provides a very strong baseline, which can be further improved by applying the principal component analysis to generate more isotropic vectors. Conclusions We have demonstrated that the proposed cascading architecture can handle the problem of overlapped annotations and further improve the overall recall and F-scores because the architecture enables the developed models to exploit more context information and forms an ensemble for creating a stronger recognizer.

scholarly journals Embedding from Language Models (ELMos)- based Dependency Parser for Indonesian Language

2021 ◽  
Vol 13 (3) ◽  
pp. 2-11
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Language Processing ◽  
Functional Relationship ◽  
Nearest Neighbor ◽  
Object Relation ◽  
Language Models ◽  
Word Representation ◽  
The Subject ◽  
Dependency Parser

The goal of dependency parsing is to seek a functional relationship among words. For instance, it tells the subject-object relation in a sentence. Parsing the Indonesian language requires information about the morphology of a word. Indonesian grammar relies heavily on affixation to combine root words with affixes to form another word. Thus, morphology information should be incorporated. Fortunately, it can be encoded implicitly by word representation. Embeddings from Language Models (ELMo) is a word representation which be able to capture morphology information. Unlike most widely used word representations such as word2vec or Global Vectors (GloVe), ELMo utilizes a Convolutional Neural Network (CNN) over characters. With it, the affixation process could ideally encoded in a word representation. We did an analysis using nearest neighbor words and T-distributed Stochastic Neighbor Embedding (t-SNE) word visualization to compare word2vec and ELMo. Our result showed that ELMo representation is richer in encoding the morphology information than it's counterpart. We trained our parser using word2vec and ELMo. To no surprise, the parser which uses ELMo gets a higher accuracy than word2vec. We obtain Unlabeled Attachment Score (UAS) at 83.08 for ELMo and 81.35 for word2vec. Hence, we confirmed that morphology information is necessary, especially in a morphologically rich language like Indonesian. Keywords: ELMo, Dependency Parser, Natural Language Processing, word2vec

scholarly journals Analisis Sentimen pada Data Saran Mahasiswa Terhadap Kinerja Departemen di Perguruan Tinggi Menggunakan Convolutional Neural Network

2021 ◽  
Vol 8 (5) ◽  
pp. 1067
Author(s):  
Yuliska Yuliska ◽  
Dini Hidayatul Qudsi ◽  
Juanda Hakim Lubis ◽  
Khairul Umum Syaliman ◽  
Nina Fadilah Najwa
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Word Embedding ◽  
Great Performance ◽  
Word Representation ◽  
The Relationship

<p class="Abstrak"><em>Review</em> atau saran dari <em>customer</em> dapat menjadi sangat penting bagi penyedia layanan, begitu pula saran dari mahasiswa mengenai layanan sebuah unit kerja di perguruan tinggi. <em>Review</em> menjadi penting karena dapat menjadi indikator kinerja penyedia layanan. Pengolahan review juga sangat penting karena dapat menjadi referensi untuk pengambilan keputusan dan peningkatan layanan yang lebih baik ke depannya. Penelitian ini menerapkan analisis sentimen pada data saran atau <em>review</em> mahasiswa terhadap kinerja unit kerja atau departemen di perguruan tinggi, yaitu Politeknik Caltex Riau. Analisis sentimen dilakukan dengan menggunakan <em>Convolutional Neural Network (CNN)</em> dan <em>word embedding</em> <em>Word2vec</em> sebagai representasi kata. <em>CNN</em> merupakan metode yang memiliki performa yang baik dalam mengklasifikasi teks, yaitu dengan teknik <em>convolutional</em> yang menggabungkan beberapa <em>window</em> kata pada kalimat dan mengambil <em>window</em> yang paling <em>representative</em>. <em>Word2Vec</em> digunakan sebagai representasi data saran dan inputan awal pada <em>CNN</em>, dimana <em>Word2Vec</em> merupakan <em>dense vectors</em> yang dapat merepresentasikan hubungan antar kata pada data saran dengan baik. Saran mahasiswa dapat mengandung kalimat yang sangat panjang, karena itu perpaduan <em>Word2Vec</em> sebagai representasi kata dan <em>CNN</em> dengan teknik <em>convolutional</em>, dapat menghasilkan representasi yang <em>representative</em> dari kalimat panjang tersebut. Penelitian ini menggunakan dua arsitektur <em>CNN</em>, yaitu <em>Simple</em> <em>CNN</em> dan <em>DoubleMax CNN</em> untuk mengidentifikasi pengaruh kompleksitas arsitektur terhadap hasil klasifikasi sentimen.  Berdasarkan hasil pengujian, <em>DoubleMax CNN</em> dapat mengklasifikasi sentimen pada saran mahasiswa dengan sangat baik, yaitu mencapai Akurasi tertinggi sebesar 98%, <em>Recall</em> 97%, <em>Precision</em> 98% dan <em>F1-Score</em> 98%.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Student’s reviews about department performance can be essential for a college for it can be used to evaluate the department performance and to take an immediate action to improve its performance. This research applies sentiment analysis in the student’s reviews of college department in Politeknik Caltex Riau. Convolutional Neural Network and Word2Vec are employed to analyze the sentiment. CNN is known for its good performance in text classification by applying a convolutional technique to the input sentences. Word2Vec is used as word representation and as an input to the CNN. Word2Vec are dense vectors which can represent the relationship between words excellently. Student’s reviews can be a long sentence; hence the combination of Word2Vec as word representation and CNN with convolutional technique can produce a representative fiture from that long sentence. This research utilizes two CNN architectures, which are Simple CNN dan DoubleMax CNN to identify the effect of the complexity of CNN architecture to final result. Our experiments show that DoubleMax CNN has a great performance in classifying sentiment in the student’s reviews with the best Accuracy value of 98%, Recall 97%, Precision 98% and F1-Score value of 98%.<strong> </strong></em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

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