The document describes a language detection library that can detect the language of texts with over 99% precision for 49 languages. It uses a Naive Bayes algorithm and character n-grams as features to classify texts into language categories. The library is open source and available for Java. It was tested on over 9,000 news articles in 49 languages with an accuracy of 99.77%.
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Tra...Edureka!
** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course **
This Edureka PPT will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics.
The following topics covered in this PPT:
1. The Evolution of Human Language
2. What is Text Mining?
3. What is Natural Language Processing?
4. Applications of NLP
5. NLP Components and Demo
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Dmitry Kan, Principal AI Scientist at Silo AI and host of the Vector Podcast [1], will give an overview of the landscape of vector search databases and their role in NLP, along with the latest news and his view on the future of vector search. Further, he will share how he and his team participated in the Billion-Scale Approximate Nearest Neighbor Challenge and improved recall by 12% over a baseline FAISS.
Presented at https://www.meetup.com/open-nlp-meetup/events/282678520/
YouTube: https://www.youtube.com/watch?v=RM0uuMiqO8s&t=179s
Follow Vector Podcast to stay up to date on this topic: https://www.youtube.com/@VectorPodcast
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Tra...Edureka!
** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course **
This Edureka PPT will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics.
The following topics covered in this PPT:
1. The Evolution of Human Language
2. What is Text Mining?
3. What is Natural Language Processing?
4. Applications of NLP
5. NLP Components and Demo
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Dmitry Kan, Principal AI Scientist at Silo AI and host of the Vector Podcast [1], will give an overview of the landscape of vector search databases and their role in NLP, along with the latest news and his view on the future of vector search. Further, he will share how he and his team participated in the Billion-Scale Approximate Nearest Neighbor Challenge and improved recall by 12% over a baseline FAISS.
Presented at https://www.meetup.com/open-nlp-meetup/events/282678520/
YouTube: https://www.youtube.com/watch?v=RM0uuMiqO8s&t=179s
Follow Vector Podcast to stay up to date on this topic: https://www.youtube.com/@VectorPodcast
The presentation describes how to install the NLTK and work out the basics of text processing with it. The slides were meant for supporting the talk and may not be containing much details.Many of the examples given in the slides are from the NLTK book (http://www.amazon.com/Natural-Language-Processing-Python-Steven/dp/0596516495/ref=sr_1_1?ie=UTF8&s=books&qid=1282107366&sr=8-1-spell ).
This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification.
(Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Words and sentences are the basic units of text. In this lecture we discuss basics of operations on words and sentences such as tokenization, text normalization, tf-idf, cosine similarity measures, vector space models and word representation
Semantic & Multilingual Strategies in Lucene/SolrTrey Grainger
When searching on text, choosing the right CharFilters, Tokenizer, stemmers, and other TokenFilters for each supported language is critical. Additional tools of the trade include language detection through UpdateRequestProcessors, parts of speech analysis, entity extraction, stopword and synonym lists, relevancy differentiation for exact vs. stemmed vs. conceptual matches, and identification of statistically interesting phrases per language. For multilingual search, you also need to choose between several strategies such as: searching across multiple fields, using a separate collection per language combination, or combining multiple languages in a single field (custom code is required for this and will be open sourced). These all have their own strengths and weaknesses depending upon your use case. This talk will provide a tutorial (with code examples) on how to pull off each of these strategies as well as compare and contrast the different kinds of stemmers, review the precision/recall impact of stemming vs. lemmatization, and describe some techniques for extracting meaningful relationships between terms to power a semantic search experience per-language. Come learn how to build an excellent semantic and multilingual search system using the best tools and techniques Lucene/Solr has to offer!
The task of keyword extraction is to automatically identify a set of terms that best describe the document. Automatic keyword extraction establishes a foundation for various natural language processing applications: information retrieval, the automatic indexing and classification of documents, automatic summarization and high-level semantic description, etc. Although the keyword extraction applications usually work on single documents (document-oriented task), keyword extraction is also applicable to a more demanding task, i.e. the keyword extraction from a whole collection of documents or from an entire web site, or from tweets from Twitter. In the era of big-data, obtaining an effective and efficient method for automatic keyword extraction from huge amounts of multi-topic textual sources is of high importance.
We proposed a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The selectivity slightly outperforms an extraction based on the standard centrality measures. Therefore, the selectivity and its modification – generalized selectivity as the node centrality measures are included in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network and it can be easily ported to new languages and used in a multilingual scenario. The true potential of the proposed SBKE method is in its generality, portability and low computation costs, which positions it as a strong candidate for preparing collections which lack human annotations for keyword extraction. Testing of the portability of the SBKE was tested on Croatian, Serbian and English texts – more precisely it was developed on Croatian News and ported for extraction from parallel abstracts of scientific publication in the Serbian and English languages.
The constructed parallel corpus of scientific abstracts with annotated keywords allows a better comparison of the performance of the method across languages since we have the controlled experimental environment and data. The achieved keyword extraction results measured with an F1 score are 49.57% for English and 46.73% for the Serbian language, if we disregard keywords that are not present in the abstracts. In case that we evaluate against the whole keyword set, the F1 scores are 40.08% and 45.71% respectively. This work shows that SBKE can be easily ported to new a language, domain and type of text in the sense of its structure. Still, there are drawbacks – the method can extract only the words that appear in the text.
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...Edureka!
( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** )
This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT:
Introduction to Big Data
What is Text Mining?
What is NLP?
Introduction to Stemming
Introduction to Lemmatization
Applications of Stemming & Lemmatization
Difference between stemming & Lemmatization
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Haystack 2018 - Algorithmic Extraction of Keywords Concepts and VocabulariesMax Irwin
Presentation as given to the Haystack Conference, which outlines research and techniques for automatic extraction of keywords, concepts, and vocabularies from text corpora.
Fine tune and deploy Hugging Face NLP modelsOVHcloud
Are you currently managing AI projects that require a lot of GPU power?
Are you tired of managing the complexity of your infrastructures, GPU instances and your Kubeflow yourself?
Need flexibility for your AI platform or SaaS solution?
OVHcloud innovates in AI by offering simple and turnkey solutions to train your models and put them into production.
This Edureka Python Programming tutorial will help you learn python and understand the various basics of Python programming with examples in detail. Below are the topics covered in this tutorial:
1. Python Installation
2. Python Variables
3. Data types in Python
4. Operators in Python
5. Conditional Statements
6. Loops in Python
7. Functions in Python
8. Classes and Objects
The presentation describes how to install the NLTK and work out the basics of text processing with it. The slides were meant for supporting the talk and may not be containing much details.Many of the examples given in the slides are from the NLTK book (http://www.amazon.com/Natural-Language-Processing-Python-Steven/dp/0596516495/ref=sr_1_1?ie=UTF8&s=books&qid=1282107366&sr=8-1-spell ).
This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification.
(Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Words and sentences are the basic units of text. In this lecture we discuss basics of operations on words and sentences such as tokenization, text normalization, tf-idf, cosine similarity measures, vector space models and word representation
Semantic & Multilingual Strategies in Lucene/SolrTrey Grainger
When searching on text, choosing the right CharFilters, Tokenizer, stemmers, and other TokenFilters for each supported language is critical. Additional tools of the trade include language detection through UpdateRequestProcessors, parts of speech analysis, entity extraction, stopword and synonym lists, relevancy differentiation for exact vs. stemmed vs. conceptual matches, and identification of statistically interesting phrases per language. For multilingual search, you also need to choose between several strategies such as: searching across multiple fields, using a separate collection per language combination, or combining multiple languages in a single field (custom code is required for this and will be open sourced). These all have their own strengths and weaknesses depending upon your use case. This talk will provide a tutorial (with code examples) on how to pull off each of these strategies as well as compare and contrast the different kinds of stemmers, review the precision/recall impact of stemming vs. lemmatization, and describe some techniques for extracting meaningful relationships between terms to power a semantic search experience per-language. Come learn how to build an excellent semantic and multilingual search system using the best tools and techniques Lucene/Solr has to offer!
The task of keyword extraction is to automatically identify a set of terms that best describe the document. Automatic keyword extraction establishes a foundation for various natural language processing applications: information retrieval, the automatic indexing and classification of documents, automatic summarization and high-level semantic description, etc. Although the keyword extraction applications usually work on single documents (document-oriented task), keyword extraction is also applicable to a more demanding task, i.e. the keyword extraction from a whole collection of documents or from an entire web site, or from tweets from Twitter. In the era of big-data, obtaining an effective and efficient method for automatic keyword extraction from huge amounts of multi-topic textual sources is of high importance.
We proposed a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The selectivity slightly outperforms an extraction based on the standard centrality measures. Therefore, the selectivity and its modification – generalized selectivity as the node centrality measures are included in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network and it can be easily ported to new languages and used in a multilingual scenario. The true potential of the proposed SBKE method is in its generality, portability and low computation costs, which positions it as a strong candidate for preparing collections which lack human annotations for keyword extraction. Testing of the portability of the SBKE was tested on Croatian, Serbian and English texts – more precisely it was developed on Croatian News and ported for extraction from parallel abstracts of scientific publication in the Serbian and English languages.
The constructed parallel corpus of scientific abstracts with annotated keywords allows a better comparison of the performance of the method across languages since we have the controlled experimental environment and data. The achieved keyword extraction results measured with an F1 score are 49.57% for English and 46.73% for the Serbian language, if we disregard keywords that are not present in the abstracts. In case that we evaluate against the whole keyword set, the F1 scores are 40.08% and 45.71% respectively. This work shows that SBKE can be easily ported to new a language, domain and type of text in the sense of its structure. Still, there are drawbacks – the method can extract only the words that appear in the text.
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...Edureka!
( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** )
This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT:
Introduction to Big Data
What is Text Mining?
What is NLP?
Introduction to Stemming
Introduction to Lemmatization
Applications of Stemming & Lemmatization
Difference between stemming & Lemmatization
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Haystack 2018 - Algorithmic Extraction of Keywords Concepts and VocabulariesMax Irwin
Presentation as given to the Haystack Conference, which outlines research and techniques for automatic extraction of keywords, concepts, and vocabularies from text corpora.
Fine tune and deploy Hugging Face NLP modelsOVHcloud
Are you currently managing AI projects that require a lot of GPU power?
Are you tired of managing the complexity of your infrastructures, GPU instances and your Kubeflow yourself?
Need flexibility for your AI platform or SaaS solution?
OVHcloud innovates in AI by offering simple and turnkey solutions to train your models and put them into production.
This Edureka Python Programming tutorial will help you learn python and understand the various basics of Python programming with examples in detail. Below are the topics covered in this tutorial:
1. Python Installation
2. Python Variables
3. Data types in Python
4. Operators in Python
5. Conditional Statements
6. Loops in Python
7. Functions in Python
8. Classes and Objects
From Programming to Modeling And Back AgainMarkus Voelter
Is programming = modeling? Are there differences, conceptual and tool-wise? Should there be differences? What if we programmed the way we model? Or vice versa? In this slidedeck I explore this question and introduce interesting developments in the space of projectional editing and modern parser technology. This leads to the concept of modular programming languages and a new way of looking at programming. I will demonstrate the idea with tools that are available today, for example TMF Xtext, JetBrains MPS and Intentional’s Domain Workbench.
NLTK: Natural Language Processing made easyoutsider2
Natural Language Toolkit(NLTK), an open source library which simplifies the implementation of Natural Language Processing(NLP) in Python is introduced. It is useful for getting started with NLP and also for research/teaching.
Building a 3-gram model for Language IdentificationKepa J. Rodriguez
Collection descriptions of archives can contain text in more than one language without the use of language identifiers. An example are cites of texts from the documents of the collections or from other collections and materials. This missed information is crucial for tasks like statistical modeling of the content of an institution and in general for several information retrieval tasks.
Similar to Language Detection Library for Java (11)
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
8. What’s “Language Detection”?
Detect language in which texts are written
also character code detection (excluded)
alias: Language identification / Language guessing
Japanese English Chinese
German Spanish Italian
Arabic Hindi Korean
9. Why Language Detection?
Purpose
For language of search criteria
Query “Java” => Hit Chinese texts...
For SPAM filter/Extract content filter
To use language-specific information(punctuations,
keywords)
Usage
Web search engine
Apache Nutch bundles a language detection module
Bulletin board
Post in English, Japanese and Chinese
10. Methods
The more languages, the more difficult
Among languages with the same script
Requires knowledge of scripts and languages
A simple method:
Matching with the dictionary in each language
Huge dictionary(inflections, compound words)
Our method:
Calcurates language probabilities from features of
spelling
Naive Bayse with character n-gram
11. Existing Language Detection
There are a few libraries of language
detection.
Usage was limited?
For only web search?
But all services will become global from now on!
Building corpus/model is a expensive work.
Requires knowledge of scripts and languages
Few languages supported & low precision
Almost 10 languages. Not including Asian ones
12. “Practical” Language Detection
99% over precision
90% is not practical. (100 of 1000 mistakes)
50 languages supported
European, Asian and so on
Fast Detection
Many documents available
Output each language’s probability
For multiple candidates
13. Language Detection Library for Java
We developed a language detection library for
Java.
Generates the language profiles from training
corpus
Profile : the probabilities of all spellings in each language
Returns the candidates and their probabilities for
given texts
49 languages supported
Open Source (Apache License 2.0)
http://code.google.com/p/language-detection/
14. Experiments
Training
49 languages from Wikipedia
That can provide a test corpus of its language
Test
200 news articles of 49 languages
Google News (24 languages)
News sites in each language
Crawling by RSS
15. Results (1)
languages # precisions items
af Afrikaans 200 199 (99.50%) en=1, af=199
ar Arabic 200 200 (100.00%) ar=200
bg Bulgarian 200 200 (100.00%) bg=200
bn Bengali 200 200 (100.00%) bn=200
cs Czech 200 200 (100.00%) cs=200
da Danish 200 179 (89.50%) da=179, no=14, en=7
de German 200 200 (100.00%) de=200
el Greek 200 200 (100.00%) el=200
en English 200 200 (100.00%) en=200
es Spanish 200 200 (100.00%) es=200
fa Persian 200 200 (100.00%) fa=200
fi Finnish 200 200 (100.00%) fi=200
fr French 200 200 (100.00%) fr=200
gu Gujarati 200 200 (100.00%) gu=200
he Hebrew 200 200 (100.00%) he=200
hi Hindi 200 200 (100.00%) hi=200
hr Croatian 200 200 (100.00%) hr=200
hu Hungarian 200 200 (100.00%) hu=200
id Indonesian 200 200 (100.00%) id=200
it Italian 200 200 (100.00%) it=200
ja Japanese 200 200 (100.00%) ja=200
kn Kannada 200 200 (100.00%) kn=200
ko Korean 200 200 (100.00%) ko=200
mk Macedonian 200 200 (100.00%) mk=200
ml Malayalam 200 200 (100.00%) ml=200
16. Results (2)
languages # precisions items
mr Marathi 200 200 (100.00%) mr=200
ne Nepali 200 200 (100.00%) ne=200
nl Dutch 200 200 (100.00%) nl=200
no Norwegian 200 199 (99.50%) da=1, no=199
pa Punjabi 200 200 (100.00%) pa=200
pl Polish 200 200 (100.00%) pl=200
pt Portuguese 200 200 (100.00%) pt=200
ro Romanian 200 200 (100.00%) ro=200
ru Russian 200 200 (100.00%) ru=200
sk Slovak 200 200 (100.00%) sk=200
so Somali 200 200 (100.00%) so=200
sq Albanian 200 200 (100.00%) sq=200
sv Swedish 200 200 (100.00%) sv=200
sw Swahili 200 200 (100.00%) sw=200
ta Tamil 200 200 (100.00%) ta=200
te Telugu 200 200 (100.00%) te=200
th Thai 200 200 (100.00%) th=200
tl Tagalog 200 200 (100.00%) tl=200
tr Turkish 200 200 (100.00%) tr=200
uk Ukrainian 200 200 (100.00%) uk=200
ur Urdu 200 200 (100.00%) ur=200
vi Vietnamese 200 200 (100.00%) vi=200
zh-cn Simplified Chinese 200 200 (100.00%) zh-cn=200
zh-tw Traditional Chinese 200 200 (100.00%) zh-tw=200
sum 9800 9777 (99.77%)
18. Language Detection with Naive Bayes
Classifies documents into “language”
categories
Categories: English, Japanese, Chinese, …
Updates the posterior probabilities of
categories by feature probabilities in each
category
������ ������k ������ (m+1) ∝ ������ ������k ������ m ⋅ ������ ������������ ������������
where ������k :category, ������:document, ������������ :feature of document
Terminates detection process if the maximum
probability(normalized) is over 0.99999
Early termination for perfomance
19. Features of Language Detection
Character n-gram
To be exact, “Unicode’s codepoint n-gram”
Much less than the size of words
Separator of
words
□T h i s □
T h i s ←1-gram
□T Th hi is s□ ←2-gram
□Th Thi his is□ ←3-gram
20. How to detect the text’s language
Each language has the peculiar characters and spelling rule.
The accented “é” is used in Spanish, Italian and so on, and not
used in English in principle.
The word that starts with “Z” is often used in German and rarely
used in English.
The word that starts with “C” and contains spell “Th” are used in
English and not used in German.
Accumulates the probabilities assigned to these features in
given text, so the guessed language is obtained as one that
has the maximum probability.
□C □L □Z Th
English 0.75 0.47 0.02 0.74
German 0.10 0.37 0.53 0.03
French 0.38 0.69 0.01 0.01
21. Improvement for Naive Detection
The above naive algorithm can detect only
90% precision.
Not “practical”
Very low precision for some languages
Japanese, Traditional Chinese, Russian, Persian, ...
Cause:
Bias and noise of training and test corpus
Improvement
Noise filter
Character normalization
22. (1) Bias of Characters
Alphabet / Arabic / Devanagari
About 30 characters
Kanji (Chinese character)
20000 characters over!
1000 times as much as Alphabets
Kanji has “zero frequency problem”
Can’t detect language of “谢谢”(Simplified
Chinese)
This character isn’t used on Wikipedia.
Name Kanji (uneven frequency)
23. Normalization with “Joyo Kanji”
Classifies “similar frequency Kanji” and normalizes
each cluster into a representative Kanji.
(1) Clustering by K-means
(2) Classification by “Joyo Kanji”
Joyo Kanji (常用漢字: regularly used Kanji)
Simplified Chinese: “现代汉语常用字表”(3500 characters)
Traditional Chinese: the first standard of Big5 (5401
characters). It includes “常用国字標準字体表” (4808
characters)
Japanese: Joyo Kanji(2136 characters) + the first standard of
JIS X 0208 (2965 characters) = 2998 characters
130 clusters
Each language has about 50 classes.
24. (2) Noise of Corpus
Removes the language-independent characters
Numeric figures, symbols, URLs and mail addresses
Latin character noise in non-Latin text
Alphabets often occur in also non-Latin text.
Java, WWW, US and so on
Remove all Latin-characters if their rate is less than 20%.
Latin character noise in Latin text
Acronyms, person’s names and place names don’t represent
feature of languages.
UNESCO, “New York” in French text
Person’s name has a various language feature (e.g. Mc- = Gaelic).
Removes all-capital words
Reduces the effect of local features by the feature-sampling
25. Normalization of Arabic Character
All Persian texts were detected as Arabic!
Persian and Arabic belong to different language families, so
it ought to be easy to discriminate them.
A high-frequency character “yeh” is assigned to
different codes in training and test corpora respectively.
In the training corpus (Wikipedia), it is assigned to “”ی
(¥u06cc, Farsi yeh).
In the test corpus (News), it is “¥( ”يu064a, Arabic yeh).
Cause: Arabic character-code CP-1256 don’t has the character
mapped to ¥u06cc, so it is substituted to ¥u064a in a general way.
Normalizes ¥u06cc(Farsi yeh) into ¥u064a(Arabic yeh)
All Persian texts are detected correctly.
27. Conclusion
We developed the language detection
library for Java.
49 languages can be detected in 99.8%
precision.
Our next product will use it (search by
language).
90% is easy. But 99% over is practical.
Ideal: Answer from the novel beautiful theory
Real: Unrefined steady way all along
28. Open Issues
Short text (e.g. twitter)
Arabic vowel signs
Text in more than one language
Source code in text
29. References
[Habash 2009] Introduction to Arabic Natual Language Processing
http://www.medar.info/conference_all/2009/Tutorial_1.pdf
[Dunning 1994] Statistical Identification of Language
[Sibun & Reynar 1996] Language identification: Examining the issues
[Martins+ 2005] Language Identification in Web Pages
千野栄一編「世界のことば100語辞典 ヨーロッパ編」
町田和彦編「図説 世界の文字とことば」
世界の文字研究会「世界の文字の図典」
町田和彦「ニューエクスプレス ヒンディー語」
中村公則「らくらくペルシャ語 文法から会話」
道広勇司「アラビア系文字の基礎知識」
http://moji.gr.jp/script/arabic/article01.html
北研二,辻井潤一「確率的言語モデル」
30. Thank you for reading
Language Detection Project Home
http://code.google.com/p/language-detection/
blog (in Japanese)
http://d.hatena.ne.jp/n_shuyo/
twitter
http://twitter.com/shuyo