Processing Methods for Multi- Label Classification of Textual Data

Total Page:16

File Type:pdf, Size:1020Kb

Processing Methods for Multi- Label Classification of Textual Data DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Comparing Feature Extraction Methods and Effects of Pre- Processing Methods for Multi- Label Classification of Textual Data MARTIN EKLUND KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Comparing Feature Extraction Methods and Effects of Pre-Processing Methods for Multi-Label Classification of Textual Data MARTIN EKLUND Master in Computer Science Date: June 27, 2018 Supervisor: Håkan Lane Examiner: Olof Bälter Swedish title: Utvärdering av Metoder för Extraktion av Särdrag och Förbehandling av Data för Multi-Taggning av Textdata School of Electrical Engineering and Computer Science iii Abstract This thesis aims to investigate how different feature extraction meth- ods applied to textual data affect the results of multi-label classifica- tion. Two different Bag of Words extraction methods are used, specif- ically the Count Vector and the TF-IDF approaches. A word embed- ding method is also investigated, called the GloVe extraction method. Multi-label classification can be useful for categorizing items, such as pieces of music or news articles, that may belong to multiple classes or topics. The effect of using different pre-processing methods is also investigated, such as the use of N-grams, stop-word elimination, and stemming. Two different classifiers, an SVM and an ANN, are used for multi-label classification using a Binary Relevance approach. The results indicate that the choice of extraction method has a meaningful impact on the resulting classifications, but that no one method consis- tently outperforms the others. Instead the results show that the GloVe extraction method performs the best for the recall metrics, while the Bag of Words methods perform the best for the precision metrics. iv Sammanfattning Detta arbete ämnar att undersöka vilken effekt olika metoder för att extrahera särdrag ur textdata har när dessa används för att multi-tagga textdatan. Två metoder baserat på Bag of Words undersöks, närmare bestämt Count Vector-metoden samt TF-IDF-metoden. Även en me- tod som använder sig av word embessings undersöks, som kallas för GloVe-metoden. Multi-taggning av data kan vara användbart när da- tan, exempelvis musikaliska stycken eller nyhetsartiklar, kan tillhöra flera klasser eller områden. Även användandet av flera olika meto- der för att förbehandla datan undersöks, såsom användandet utav N- gram, eliminering av icke-intressanta ord, samt transformering av ord med olika böjningsformer till gemensam stamform. Två olika klassifi- cerare, en SVM samt en ANN, används för multi-taggningen genom använding utav en metod kallad Binary Relevance. Resultaten visar att valet av metod för extraktion av särdrag har en betydelsefull roll för den resulterande multi-taggningen, men att det inte finns en me- tod som ger bäst resultat genom alla tester. Istället indikerar resultaten att extraktionsmetoden baserad på GloVe presterar bäst när det gäl- ler ’recall’-mätvärden, medan Bag of Words-metoderna presterar bäst gällade ’precision’-mätvärden. Contents 1 Introduction 1 1.1 Problem Statement . .2 1.2 Scope . .2 1.3 Objective . .3 2 Background 4 2.1 Multi-Label Classification . .4 2.1.1 Methods . .5 2.2 Content-Based Recommendation . .5 2.2.1 Exploitation and Exploration . .6 2.3 Pre-Processing . .7 2.3.1 Tokenization and N-grams . .7 2.3.2 Stemming . .8 2.3.3 Stop-Word Elimination . .8 2.4 Feature Extraction . .8 2.4.1 Bag of Words . .9 2.4.2 Term Frequency-Inverse Document Frequency . .9 2.4.3 Word Embeddings and GloVe . 11 2.5 Classifiers . 12 2.5.1 Support Vector Machine . 12 2.5.2 Artificial Neural Networks . 13 2.6 Related Work . 14 2.6.1 Word Embeddings for Single-Label Classification 14 2.6.2 Bag of Words for Multi-Label Classification . 15 2.6.3 Research Gap . 15 3 Method 16 3.1 Dataset . 16 3.2 Pre-Processing . 17 v vi CONTENTS 3.3 Extraction Methods . 17 3.3.1 TF-IDF . 17 3.3.2 Bag of Words/Count Vector . 18 3.3.3 GloVe . 18 3.4 Classifiers . 18 3.5 Evaluation . 19 3.5.1 Precision . 19 3.5.2 Recall . 19 3.5.3 F-Score . 20 4 Results 21 4.1 TF-IDF . 21 4.2 Count Vector . 21 4.3 GloVe . 25 4.4 Effect of N-grams . 25 4.5 Stop-Words . 25 4.6 Stemming . 28 4.7 Best Scores . 30 5 Discussion 32 5.1 Effects of Preprocessing . 32 5.1.1 N-grams . 32 5.1.2 Stop-Word Elimination . 33 5.1.3 Stemming . 33 5.2 Extraction Methods . 33 5.2.1 TF-IDF and Count Vector . 33 5.2.2 Bag of Words vs. Word Embeddings . 34 5.3 Concerns . 35 5.4 Future Work . 35 6 Conclusion 36 Bibliography 38 A Appended Material 41 Chapter 1 Introduction The amount of digital content available on the Internet is steadily grow- ing, and it can prove challenging to make the best use of the vast amount of data that is available. One way to handle this problem is to classify data into different categories, thus giving a better overview of what kinds of data are available. This can in turn for instance enable users of a news site to better filter out the articles that they are inter- ested in, or enable users on social media to locate photos of themselves. In order not to have to do this procedure of categorization manually, one can instead employ machine learning techniques to automate the process. Such techniques usually require distinguishing features of an object in order to be able to classify it. There are several different ways to extract features from different types of data, and different methods may prove suitable in different situations. For instance when classifying images of apples and or- anges, a crude extraction method could be to take the average pixel values of the images. If the average pixel value is close to orange, then the image would be classified as an orange, and if it is closer to green then it would be classified as an apple. This method would however also most likely classify a tiger as an orange, since it does not take into account any other feature than color into consideration. By choosing a more appropriate extraction method this could hopefully be avoided. Instead of only taking the average pixel values into account one could also consider features such as the shape, size and texture of the object. Taking these features into account, an image of a tiger would most likely not be classified as an orange. Categorized data items can, among other things, be used to pro- 1 2 CHAPTER 1. INTRODUCTION vide recommendations of similar items to a user depending on which categories the user has previously shown an interest in. By using a multi-label approach, hopefully, the problem of Exploitation and Ex- ploration described in section 2.2.1 could be somewhat mitigated. This could be done by recommending items that might not be the ones most similar to the users viewing history, but that still lie relatively close to other categories that the user has shown a previous interest in. 1.1 Problem Statement The aim of this report is to evaluate how different methods for feature extraction affect a multi-label classification problem with textual data. The effect of different pre-processing methods is also investigated. If the choice of extraction method has a significant impact on the final classification results, then it might be better to choose the right extrac- tion method rather than spend too much time optimizing the classifier itself. The questions that this report will attempt to answer are: • Which one of the three feature extraction methods (Count Vector, TF-IDF and GloVe) performs the best? • Does one of these feature extraction methods perform the best even when used with different classifier models? • Are commonly used pre-processing methods always useful when applied to different feature extraction methods? 1.2 Scope The main goal of this thesis is to examine the effect of feature extrac- tion methods for multi-label classification for textual data. Three fea- ture extraction methods are evaluated in conjunction with two classi- fier models. It would be possible to include more classifier models, but hopefully two are enough to examine whether or not extraction methods yield similar results when used with different classifier. The resulting classifications may in turn aid in building a simple recommendation system, but it is outside the scope of this thesis to CHAPTER 1. INTRODUCTION 3 evaluate such a system. Instead this work is to be viewed as examin- ing the classification foundation for such a system. This work is being done in association with the Swedish Pensions Agency (Pensionsmyn- digheten), who are interested in developing a prototype for a recom- mender system. 1.3 Objective The objective of this report is to investigate how different approaches for feature extraction affects the results of multi-label classification. Naturally you want as good results as possible when doing classifi- cation. If the choice of feature extraction method has a great impact, it might be worth it to spend more time focusing on selecting an appro- priate feature extraction method rather than spending a lot of time op- timizing a certain classifier. This report also investigates if it is always useful to apply commonly used pre-processing methods to text data when different extraction methods are used. Hopefully the results of this work could prove useful for trying to better classify multi-label data. Chapter 2 Background 2.1 Multi-Label Classification In single-label classification a data sample is assigned only one label (or category) from a set of disjoint labels.
Recommended publications
  • 10 Oriented Principal Component Analysis for Feature Extraction
    10 Oriented Principal Component Analysis for Feature Extraction Abstract- Principal Components Analysis (PCA) is a popular unsupervised learning technique that is often used in pattern recognition for feature extraction. Some variants have been proposed for supervising PCA to include class information, which are usually based on heuristics. A more principled approach is presented in this paper. Suppose that we have a pattern recogniser composed of a linear network for feature extraction and a classifier. Instead of designing each module separately, we perform global training where both learning systems cooperate in order to find those components (Oriented PCs) useful for classification. Experimental results, using artificial and real data, show the potential of the proposed learning system for classification. Since the linear neural network finds the optimal directions to better discriminate classes, the global-trained pattern recogniser needs less Oriented PCs (OPCs) than PCs so the classifier’s complexity (e.g. capacity) is lower. In this way, the global system benefits from a better generalisation performance. Index Terms- Oriented Principal Components Analysis, Principal Components Neural Networks, Co-operative Learning, Learning to Learn Algorithms, Feature Extraction, Online gradient descent, Pattern Recognition, Compression. Abbreviations- PCA- Principal Components Analysis, OPC- Oriented Principal Components, PCNN- Principal Components Neural Networks. 1. Introduction In classification, observations belong to different classes. Based on some prior knowledge about the problem and on the training set, a pattern recogniser is constructed for assigning future observations to one of the existing classes. The typical method of building this system consists in dividing it into two main blocks that are usually trained separately: the feature extractor and the classifier.
    [Show full text]
  • Feature Selection/Extraction
    Feature Selection/Extraction Dimensionality Reduction Feature Selection/Extraction • Solution to a number of problems in Pattern Recognition can be achieved by choosing a better feature space. • Problems and Solutions: – Curse of Dimensionality: • #examples needed to train classifier function grows exponentially with #dimensions. • Overfitting and Generalization performance – What features best characterize class? • What words best characterize a document class • Subregions characterize protein function? – What features critical for performance? – Subregions characterize protein function? – Inefficiency • Reduced complexity and run-time – Can’t Visualize • Allows ‘intuiting’ the nature of the problem solution. Curse of Dimensionality Same Number of examples Fill more of the available space When the dimensionality is low Selection vs. Extraction • Two general approaches for dimensionality reduction – Feature extraction: Transforming the existing features into a lower dimensional space – Feature selection: Selecting a subset of the existing features without a transformation • Feature extraction – PCA – LDA (Fisher’s) – Nonlinear PCA (kernel, other varieties – 1st layer of many networks Feature selection ( Feature Subset Selection ) Although FS is a special case of feature extraction, in practice quite different – FSS searches for a subset that minimizes some cost function (e.g. test error) – FSS has a unique set of methodologies Feature Subset Selection Definition Given a feature set x={xi | i=1…N} find a subset xM ={xi1, xi2, …, xiM}, with M<N, that optimizes an objective function J(Y), e.g. P(correct classification) Why Feature Selection? • Why not use the more general feature extraction methods? Feature Selection is necessary in a number of situations • Features may be expensive to obtain • Want to extract meaningful rules from your classifier • When you transform or project, measurement units (length, weight, etc.) are lost • Features may not be numeric (e.g.
    [Show full text]
  • Feature Extraction (PCA & LDA)
    Feature Extraction (PCA & LDA) CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline What is feature extraction? Feature extraction algorithms Linear Methods Unsupervised: Principal Component Analysis (PCA) Also known as Karhonen-Loeve (KL) transform Supervised: Linear Discriminant Analysis (LDA) Also known as Fisher’s Discriminant Analysis (FDA) 2 Dimensionality Reduction: Feature Selection vs. Feature Extraction Feature selection Select a subset of a given feature set Feature extraction (e.g., PCA, LDA) A linear or non-linear transform on the original feature space ⋮ ⋮ → ⋮ → ⋮ = ⋮ Feature Selection Feature ( <) Extraction 3 Feature Extraction Mapping of the original data onto a lower-dimensional space Criterion for feature extraction can be different based on problem settings Unsupervised task: minimize the information loss (reconstruction error) Supervised task: maximize the class discrimination on the projected space In the previous lecture, we talked about feature selection: Feature selection can be considered as a special form of feature extraction (only a subset of the original features are used). Example: 0100 X N 4 XX' 0010 X' N 2 Second and thirth features are selected 4 Feature Extraction Unsupervised feature extraction: () () A mapping : ℝ →ℝ ⋯ Or = ⋮⋱⋮ Feature Extraction only the transformed data () () () () ⋯ ′ ⋯′ = ⋮⋱⋮ () () ′ ⋯′ Supervised feature extraction: () () A mapping : ℝ →ℝ ⋯ Or = ⋮⋱⋮ Feature Extraction only the transformed
    [Show full text]
  • Feature Extraction for Image Selection Using Machine Learning
    Master of Science Thesis in Electrical Engineering Department of Electrical Engineering, Linköping University, 2017 Feature extraction for image selection using machine learning Matilda Lorentzon Master of Science Thesis in Electrical Engineering Feature extraction for image selection using machine learning Matilda Lorentzon LiTH-ISY-EX--17/5097--SE Supervisor: Marcus Wallenberg ISY, Linköping University Tina Erlandsson Saab Aeronautics Examiner: Lasse Alfredsson ISY, Linköping University Computer Vision Laboratory Department of Electrical Engineering Linköping University SE-581 83 Linköping, Sweden Copyright © 2017 Matilda Lorentzon Abstract During flights with manned or unmanned aircraft, continuous recording can result in a very high number of images to analyze and evaluate. To simplify image analysis and to minimize data link usage, appropriate images should be suggested for transfer and further analysis. This thesis investigates features used for selection of images worthy of further analysis using machine learning. The selection is done based on the criteria of having good quality, salient content and being unique compared to the other selected images. The investigation is approached by implementing two binary classifications, one regard- ing content and one regarding quality. The classifications are made using support vector machines. For each of the classifications three feature extraction methods are performed and the results are compared against each other. The feature extraction methods used are histograms of oriented gradients, features from the discrete cosine transform domain and features extracted from a pre-trained convolutional neural network. The images classified as both good and salient are then clustered based on similarity measures retrieved using color coherence vectors. One image from each cluster is retrieved and those are the result- ing images from the image selection.
    [Show full text]
  • Time Series Feature Extraction for Industrial Big Data (Iiot) Applications
    Time Series Feature Extraction for Industrial Big Data (IIoT) Applications [1] Term paper for the subject „Current Topics of Data Engineering” In the course of studies MSc. Data Engineering at the Jacobs University Bremen Markus Sun Born on 07.11.1994 in Achim Student/ Registration number: 30003082 1. Reviewer: Prof. Dr. Adalbert F.X. Wilhelm 2. Reviewer: Prof. Dr. Stefan Kettemann Time Series Feature Extraction for Industrial Big Data (IIoT) Applications __________________________________________________________________________ Table of Content Abstract ...................................................................................................................... 4 1 Introduction ...................................................................................................... 4 2 Main Topic - Terminology ................................................................................. 5 2.1 Sensor Data .............................................................................................. 5 2.2 Feature Selection and Feature Extraction...................................................... 5 2.2.1 Variance Threshold.............................................................................................................. 7 2.2.2 Correlation Threshold .......................................................................................................... 7 2.2.3 Principal Component Analysis (PCA) .................................................................................. 7 2.2.4 Linear Discriminant Analysis
    [Show full text]
  • Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards
    agronomy Article Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards Hong-Kun Lyu 1,*, Sanghun Yun 1 and Byeongdae Choi 1,2 1 Division of Electronics and Information System, ICT Research Institute, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea; [email protected] (S.Y.); [email protected] (B.C.) 2 Department of Interdisciplinary Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea * Correspondence: [email protected]; Tel.: +82-53-785-3550 Received: 20 November 2020; Accepted: 4 December 2020; Published: 8 December 2020 Abstract: Deep learning and machine learning (ML) technologies have been implemented in various applications, and various agriculture technologies are being developed based on image-based object recognition technology. We propose an orchard environment free space recognition technology suitable for developing small-scale agricultural unmanned ground vehicle (UGV) autonomous mobile equipment using a low-cost lightweight processor. We designed an algorithm to minimize the amount of input data to be processed by the ML algorithm through low-resolution grayscale images and image binarization. In addition, we propose an ML feature extraction method based on binary pixel quantification that can be applied to an ML classifier to detect free space for autonomous movement of UGVs from binary images. Here, the ML feature is extracted by detecting the local-lowest points in segments of a binarized image and by defining 33 variables, including local-lowest points, to detect the bottom of a tree trunk. We trained six ML models to select a suitable ML model for trunk bottom detection among various ML models, and we analyzed and compared the performance of the trained models.
    [Show full text]
  • Towards Reproducible Meta-Feature Extraction
    Journal of Machine Learning Research 21 (2020) 1-5 Submitted 5/19; Revised 12/19; Published 1/20 MFE: Towards reproducible meta-feature extraction Edesio Alcobaça [email protected] Felipe Siqueira [email protected] Adriano Rivolli [email protected] Luís P. F. Garcia [email protected] Jefferson T. Oliva [email protected] André C. P. L. F. de Carvalho [email protected] Institute of Mathematical and Computer Sciences University of São Paulo Av. Trabalhador São-carlense, 400, São Carlos, São Paulo 13560-970, Brazil Editor: Alexandre Gramfort Abstract Automated recommendation of machine learning algorithms is receiving a large deal of attention, not only because they can recommend the most suitable algorithms for a new task, but also because they can support efficient hyper-parameter tuning, leading to better machine learning solutions. The automated recommendation can be implemented using meta-learning, learning from previous learning experiences, to create a meta-model able to associate a data set to the predictive performance of machine learning algorithms. Al- though a large number of publications report the use of meta-learning, reproduction and comparison of meta-learning experiments is a difficult task. The literature lacks extensive and comprehensive public tools that enable the reproducible investigation of the differ- ent meta-learning approaches. An alternative to deal with this difficulty is to develop a meta-feature extractor package with the main characterization measures, following uniform guidelines that facilitate the use and inclusion of new meta-features. In this paper, we pro- pose two Meta-Feature Extractor (MFE) packages, written in both Python and R, to fill this lack.
    [Show full text]
  • Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data Robert T
    Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data Robert T. Olszewski February 2001 CMU-CS-01-108 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Thesis Committee: Roy Maxion, co-chair Dan Siewiorek, co-chair Christos Faloutsos David Banks, DOT Copyright c 2001 Robert T. Olszewski This research was sponsored by the Defense Advanced Project Research Agency (DARPA) and the Air Force Research Laboratory (AFRL) under grant #F30602-96-1-0349, the National Science Foundation (NSF) under grant #IRI-9224544, and the Air Force Laboratory Graduate Fellowship Program sponsored by the Air Force Office of Scientific Research (AFOSR) and conducted by the Southeastern Center for Electrical Engineering Education (SCEEE). The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of DARPA, AFRL, NSF, AFOSR, SCEEE, or the U.S. government. Keywords: Structural pattern recognition, classification, feature extraction, time series, Fourier transformation, wavelets, semiconductor fabrication, electrocardiography. Abstract Pattern recognition encompasses two fundamental tasks: description and classification. Given an object to analyze, a pattern recognition system first generates a description of it (i.e., the pat- tern) and then classifies the object based on that description (i.e., the recognition). Two general approaches for implementing pattern recognition systems, statistical and structural, employ differ- ent techniques for description and classification. Statistical approaches to pattern recognition use decision-theoretic concepts to discriminate among objects belonging to different groups based upon their quantitative features.
    [Show full text]
  • Feature Extraction Using Dimensionality Reduction Techniques: Capturing the Human Perspective
    Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2015 Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective Ashley B. Coleman Wright State University Follow this and additional works at: https://corescholar.libraries.wright.edu/etd_all Part of the Computer Engineering Commons, and the Computer Sciences Commons Repository Citation Coleman, Ashley B., "Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective" (2015). Browse all Theses and Dissertations. 1630. https://corescholar.libraries.wright.edu/etd_all/1630 This Thesis is brought to you for free and open access by the Theses and Dissertations at CORE Scholar. It has been accepted for inclusion in Browse all Theses and Dissertations by an authorized administrator of CORE Scholar. For more information, please contact [email protected]. FEATURE EXTRACTION USING DIMENSIONALITY REDUCITON TECHNIQUES: CAPTURING THE HUMAN PERSPECTIVE A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science By Ashley Coleman B.S., Wright State University, 2010 2015 Wright State University WRIGHT STATE UNIVERSITY GRADUATE SCHOOL December 11, 2015 I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY SUPERVISION BY Ashley Coleman ENTI TLED Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science. Pascal Hitzler, Ph.D. Thesis Advisor Mateen Rizki, Ph.D. Chair, Department of Computer Science Committee on Final Examination John Gallagher, Ph.D. Thesis Advisor Mateen Rizki, Ph.D. Chair, Department of Computer Science Robert E. W. Fyffe, Ph.D. Vice President for Research and Dean of the Graduate School Abstract Coleman, Ashley.
    [Show full text]
  • Unsupervised Feature Extraction for Reinforcement Learning
    Faculteit Wetenschappen en Bio-ingenieurswetenschappen Vakgroep Computerwetenschappen Unsupervised Feature Extraction for Reinforcement Learning Proefschrift ingediend met het oog op het behalen van de graad van Master of Science in de Ingenieurswetenschappen: Computerwetenschappen Yoni Pervolarakis Promotor: Prof. Dr. Peter Vrancx Prof. Dr. Ann Now´e Juni 2016 Faculty of Science and Bio-Engineering Sciences Department of Computer Science Unsupervised Feature Extraction for Reinforcement Learning Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in de Ingenieurswetenschappen: Computerwetenschappen Yoni Pervolarakis Promotor: Prof. Dr. Peter Vrancx Prof. Dr. Ann Now´e June 2016 Abstract When using high dimensional features chances are that most of the features are not important to a specific problem. To eliminate those features and potentially finding better features different possibilities exist. For example, feature extraction that will transform the original input features to a new smaller dimensional feature set or even a feature selection method where only features are taken that are more important than other features. This can be done in a supervised or unsupervised manner. In this thesis, we will investigate if we can use autoencoders as a means of unsupervised feature extraction method on data that is not necessary interpretable. These new features will then be tested in a Reinforcement Learning environment. This data will be represented as RAM states and are blackbox since we cannot understand them. The autoencoders will receive a high dimensional feature set and will transform it into a lower dimension, these new features will be given to an agent who will make use of those features and tries to learn from them.
    [Show full text]
  • Alignment-Based Topic Extraction Using Word Embedding
    Alignment-Based Topic Extraction Using Word Embedding [0000 0003 3108 8407] [0000 0002 8408 3944] Tyler Newman − − − and Paul Anderson − − − College of Charleston, Charleston SC 29424, USA [email protected] [email protected] Abstract. Being able to extract targeted topics from text can be a useful tool for understanding the large amount of textual data that exists in various domains. Many methods have surfaced for building frameworks that can successfully extract this topic data. However, it is often the case that a large number of training samples must be labeled properly, which requires both time and domain knowledge. This paper introduces new alignment-based methods for predicting topics within textual data that minimizes the dependence upon a large, properly-labeled training set. Leveraging Word2Vec word embeddings trained using unlabeled data in a semi-supervised approach, we are able to reduce the amount of labeled data necessary during the text annotation process. This allows for higher prediction levels to be attained in a more time-efficient manner with a smaller sample size. Our method is evaluated on both a publicly available Twitter sentiment classification dataset and on a real estate text classification dataset with 30 topics. Keywords: Topic extraction · Text annotation · Text classification · Word vectors · Text tagging 1 Introduction Finding specific topics within textual data is an important task for many do- mains. A multitude of approaches for achieving this task have appeared in recent years [14], no doubt due to the ever growing amount of textual data available to organizations and researchers. In the most straightforward case, topic labels are known a priori and non-domain experts can be trained to manually label examples using systems such as Mechanical Turk [4].
    [Show full text]
  • Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification
    International Journal of Environmental Research and Public Health Article Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification Kwang Ho Park 1 , Erdenebileg Batbaatar 1 , Yongjun Piao 2, Nipon Theera-Umpon 3,4,* and Keun Ho Ryu 4,5,6,* 1 Database and Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea; [email protected] (K.H.P.); [email protected] (E.B.) 2 School of Medicine, Nankai University, Tianjin 300071, China; [email protected] 3 Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand 4 Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand 5 Data Science Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh 700000, Vietnam 6 Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea * Correspondence: [email protected] (N.T.-U.); [email protected] or [email protected] (K.H.R.) Abstract: Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoi- etic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques. Furthermore, due to the overfitting problem of small samples, in case of a minor cancer, it does not have enough sample material for building a classification model. Therefore, Citation: Park, K.H.; Batbaatar, E.; we propose not only to build a classification model for five major subtypes using two kinds of losses, Piao, Y.; Theera-Umpon, N.; Ryu, K.H.
    [Show full text]