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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

5 Useful Tools To Provide Effective Data Visualization For Translators

anna_medina Last Updated : 25 Jun, 2020
7 min read

Introduction

Language translators are in a world of their own. Our task involves meticulous preparation and quality checks before the final touches are put. We have a small set of applications and online tools that we use to check the quality of our work. 

It’s quite possible that these tools have repeatedly saved you from working all night. I know I definitely fall in this category, as I use CAT and other tools on a daily basis! By automating my workflow and helping with checks and other things, these tools and applications have helped me spend more time focusing on the more important stuff.

data_visualization_translators

One thing that I’m always interested in – leveraging more useful apps for translation. It makes a ton of sense to me because the tools I use are actually helping me to be more productive and deliver quality work for clients.

Recently, I’ve come across a fascinating topic: the role of visualization in helping translators to come up with more accurate and creative translations. And that’s the idea behind this article.

 

The Role of Visualization in Translation

So, as I was looking for more options to try out, I found this paper called “Translation through Visualization.” It was written by a professor from Johannes Gutenberg University in Germany, who talked about one important aspect in professional translation: problem-solving.

Let’s face it, the job of a translator involves a lot of problem-solving, and this is where we need to be good.

According to the document, one thing that could help translators to solve problems more effectively is visualization.

Let me give you a simple example. 

Let’s suppose that you’re translating a technical manual involving a complex engine. Obviously, there are lots of different parts of that engine that you have to translate correctly, but, to do so, you need to understand how the entire machine works.

What an interesting project to work on, right?

Sure, the best way is to refer to pictures, schematic drawings, videos, and other visuals depicting the engine. By analyzing them, you can actually understand the role of each component, therefore, be in a better position to deliver a more accurate and quality translation. 

We use visuals to communicate complex machines because our mind is good at processing them. In fact, about 65 percent of the population are visual learners, so they prefer to learn and remember information through visual communication. 

So, chances are that you’re one of them, too, so you’d appreciate some visuals to aid with delivering quality translation. 

In addition to helping with the accuracy, visuals are also great at fostering creativity in translation. For example, looking at a situation, object, person, poster, or whatever can inspire to come up with a creative translation. 

According to the document, a translator cannot always rely on words to capture the essence of something, so using visuals is an excellent tool for helping with that.

That’s why I decided to create this list of tools for visualization of data and text that you can use to extract meanings and make your work easier.

 

Data Visualization Tools for Translators

InfraNodus

A Textexture project, InfraNodus has been created with one purpose: convert text and data into a visual summary for discovering insights. It works through a number of powerful network analysis algorithms and text analysis tools to generate a network graph, which the translator can use to improve their thinking process.

Using InfraNodus is actually pretty simple: 

  • Add a text or data to the tool (integration with Google, Twitter, Gephi, and Twitter are available, too)
  • The tool converts the input into a graph where the connections between the co-occurrences in the textual content are shown
  • The most important data or text are shown larger to help with navigation through the graph. The words with similar meaning or those that occur more often are grouped into separate clusters with unique colors
  • Navigating through InfraNodus’s features is easy when the graph is complete. For example, by going to the Insight main menu item, you’ll get access to analytical features. You can use them to identify structural gaps in the graphs, understand meanings and contexts, and generate new ideas on how to approach translating the text

The experiences of translators with InfraNodus have been positive, too.

“After trying InfraNodus myself, I can say that it could be used to create easy-to-navigate mindmaps, visualize the notes that you made to reveal the main concepts,” says Janusz Gostkowski, a translator from TheWordPoint. “And, of course, I can visualize data to get help with understanding the context.” 

 

“Here’s my advice: do your best not to cram lines in your visualization just for the sake of it,” shares Joanna Awona, a content localization expert from Profitner. “You might easily end up with tons of unrelated data points.”

One last thing: keep in mind that InfraNodus supports 7 languages: English, French, Spanish, Swedish, Russian, and Portuguese.

 

2. SDL Trados (and Plugins)

SDL Trados is a must-have tool in every professional translator’s arsenal. Personally, I started using it many years ago and I still use it for a lot of my projects, especially those involving data tables and other complex content types.

Trados is a CAT (Computer Assisted Translation) software that is also an excellent tool for data visualization. Getting a visual overview of the translated document is available as you work on it from View > Preview (shown by a red arrow in the below image).

The preview of the document is shown in the bottom right corner.

The function opens the document with your progress as shown, so you can see how the translated data and copy looks visually. The window with the previous looks small here, but you can expand it to get a more detailed view. 

So, this way you can visualize data as you translate, which helps with the overall understanding of the content, structure, and formatting.

Plugins

SDL has an app store for Trados that you should definitely check out. In addition to such useful tools like multiterm glossaries, vocabulary management, and publication mappings, it also has data visualization plugins like TAUS DQF.

In addition to helping with tracking quality and productivity, it also allows creating custom visual reports regarding your performance. It analyzes all projects you’ve worked on and generates tables, graphs, and other visuals in a report similar to the one below.

This can help with monitoring your work and discovering where you might be underperforming or not meeting your personal goals.

 

3. OverviewDocs

Initially built for investigative journalists, OverviewDocs is an open-source visualization and analysis tool for processing textual documents (however, only you can view the documents you upload). It supports a wide range of file types and many languages as well and includes its own search engine for discovering documents. 

The WordCloud feature is one of the most interesting features for translators. It’s basically a plugin that is able to search through thousands of documents to create word clouds for categorizing and discovering texts with the same word. 

However, translators aren’t the only ones using the WordCloud. According to Michael Domm, the head of the marketing department at OmniStar, a referral marketing software, they tracked documents for positive customer feedback indications and discovering the best-performing affiliates.

Another worthy feature is text visualization. To help you get acquainted with it, OverviewDocs comes with a few default texts (but you can also start processing your own texts right away).

Also, you can look for multiple search terms in documents at once, which could be useful to discover content in poorly scanned texts.

 

4. Text Analyzer

While not entirely a text visualization tool, Text Analyzer is a useful option for getting detailed text-related stats and information. For translators, it could become a go-to tool for quoting, text complexity assessment, word frequency, and other stats.

I’ve analyzed Shakespeare’s text and here’s the list of stats generated by the tool:

  • Word counts
  • Number of different words
  • Readability according to Gunning Fox Index (an average of the number of words per sentence and the number of long words per word)
  • Complexity Factors (lexical density)
  • Average syllables per word
  • Min/max sentence length
  • Frequency and top words (the user can look for specific words, too)
  • Word length stats
  • Word phrases frequency

Text Analyzer also allows getting stats for web pages.

 

5. Infogram

Infogram is a stylish tool for data visualization, able to create infographics, reports, slides, maps, dashboards, and social media visuals with your data. Just input your data and select the type of visual that you’d like to create, and Infogram will generate quality content.

The good thing is that Infogram comes with a range of templates for your content, so you won’t have to think about how to represent the data. Not only can the tool help you with understanding texts, but it also represents your work in a compelling, easy-to-understand way.

 

End Notes

The visual representation of the content of a long and complex text document is something that many translators can benefit from. Whether it’s a preview generated in Trados or a conversion into a word cloud, data visualization tools can help decipher both structured and unstructured data.

I hope that at least some tools from this list will also improve your productivity and save you tons of time!

 

About the Author

 

Anna Medina

Anna is a specialist in different types of writing. She graduated from the Interpreters Department, but creative writing became her favorite type of work. Now she improves her skills while working as a freelance writer and translator for TheWordPoint and has free time for another work, as well. She always does her best in the posts and articles.

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