This document discusses a proposed architecture for detecting emotions from Tamil news text using a neural network. The key inputs to the neural network are outputs from a domain classifier, two sentiment analyzers (one that detects negation and one that detects polarity), and three affect taggers. The neural network is trained to assign weights to these features to determine the overall emotion expressed in the text. A two-dimensional animated face generator would then display the detected emotion visually. The document reviews related work on emotion detection from text and discusses why this proposed neural network approach may be better suited for a morphologically rich language like Tamil compared to other methods.
MULTILINGUAL SPEECH IDENTIFICATION USING ARTIFICIAL NEURAL NETWORKijitcs
Speech technology is an emerging technology and automatic speech recognition has made advances in recent years. Many researches has been performed for many foreign and regional languages. But at present the multilingual speech processing technology has been attracting for research purpose. This paper tries to propose a methodology for developing a bilingual speech identification system for Assamese and English language based on artificial neural network.
DataFest 2017. Introduction to Natural Language Processing by Rudolf Eremyanrudolf eremyan
The objective of this workshop is to show how natural language processing applied in modern applications such as Google Search, Apple Siri, Bing Translator and etc. During the workshop we will go through history if natural language processing, talk about typical problems, consider classical approaches and methods, and compare them with state-of-the-art deep learning techniques.
Author: Rudolf Eremyan
Email: eremyan.rudolf@gmail.com
Phone: +995599607066
LinkedIn: https://www.linkedin.com/in/rudolferemyan/
DataFest Tbilisi 2017 website: https://datafest.ge
Anaphora Resolution is a process of finding referents in discourse. In computational linguistic, Anaphora
resolution is complex and challenging task. This paper focuses on pronominal anaphora resolution. It is a
subpart of anaphora resolution where pronouns are referred to noun referents. Including anaphora
resolution into many applications like automatic summarization, opinion mining, machine translation,
question answering systems etc. increase their accuracy by 10%. Related work in this field has been done
in many languages. This paper focuses on resolving anaphora for Punjabi language. A model is proposed
for resolving anaphora and an experiment is conducted to measure the accuracy of the system. The model
uses two factors: Recency and Animistic knowledge. Recency factor works on the concept of Lappin Leass
approach and for introducing animistic knowledge gazetteer method is used. The experiment is conducted
on a Punjabi story containing more than 1000 words and result is drawn with the future directions.
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text EditorWaqas Tariq
In recent decades speech interactive systems have gained increasing importance. Performance of an ASR system mainly depends on the availability of large corpus of speech. The conventional method of building a large vocabulary speech recognizer for any language uses a top-down approach to speech. This approach requires large speech corpus with sentence or phoneme level transcription of the speech utterances. The transcriptions must also include different speech order so that the recognizer can build models for all the sounds present. But, for Telugu language, because of its complex nature, a very large, well annotated speech database is very difficult to build. It is very difficult, if not impossible, to cover all the words of any Indian language, where each word may have thousands and millions of word forms. A significant part of grammar that is handled by syntax in English (and other similar languages) is handled within morphology in Telugu. Phrases including several words (that is, tokens) in English would be mapped on to a single word in Telugu.Telugu language is phonetic in nature in addition to rich in morphology. That is why the speech technology developed for English cannot be applied to Telugu language. This paper highlights the work carried out in an attempt to build a voice enabled text editor with capability of automatic term suggestion. Main claim of the paper is the recognition enhancement process developed by us for suitability of highly inflecting, rich morphological languages. This method results in increased speech recognition accuracy with very much reduction in corpus size. It also adapts Telugu words to the database dynamically, resulting in growth of the corpus.
MULTILINGUAL SPEECH IDENTIFICATION USING ARTIFICIAL NEURAL NETWORKijitcs
Speech technology is an emerging technology and automatic speech recognition has made advances in recent years. Many researches has been performed for many foreign and regional languages. But at present the multilingual speech processing technology has been attracting for research purpose. This paper tries to propose a methodology for developing a bilingual speech identification system for Assamese and English language based on artificial neural network.
DataFest 2017. Introduction to Natural Language Processing by Rudolf Eremyanrudolf eremyan
The objective of this workshop is to show how natural language processing applied in modern applications such as Google Search, Apple Siri, Bing Translator and etc. During the workshop we will go through history if natural language processing, talk about typical problems, consider classical approaches and methods, and compare them with state-of-the-art deep learning techniques.
Author: Rudolf Eremyan
Email: eremyan.rudolf@gmail.com
Phone: +995599607066
LinkedIn: https://www.linkedin.com/in/rudolferemyan/
DataFest Tbilisi 2017 website: https://datafest.ge
Anaphora Resolution is a process of finding referents in discourse. In computational linguistic, Anaphora
resolution is complex and challenging task. This paper focuses on pronominal anaphora resolution. It is a
subpart of anaphora resolution where pronouns are referred to noun referents. Including anaphora
resolution into many applications like automatic summarization, opinion mining, machine translation,
question answering systems etc. increase their accuracy by 10%. Related work in this field has been done
in many languages. This paper focuses on resolving anaphora for Punjabi language. A model is proposed
for resolving anaphora and an experiment is conducted to measure the accuracy of the system. The model
uses two factors: Recency and Animistic knowledge. Recency factor works on the concept of Lappin Leass
approach and for introducing animistic knowledge gazetteer method is used. The experiment is conducted
on a Punjabi story containing more than 1000 words and result is drawn with the future directions.
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text EditorWaqas Tariq
In recent decades speech interactive systems have gained increasing importance. Performance of an ASR system mainly depends on the availability of large corpus of speech. The conventional method of building a large vocabulary speech recognizer for any language uses a top-down approach to speech. This approach requires large speech corpus with sentence or phoneme level transcription of the speech utterances. The transcriptions must also include different speech order so that the recognizer can build models for all the sounds present. But, for Telugu language, because of its complex nature, a very large, well annotated speech database is very difficult to build. It is very difficult, if not impossible, to cover all the words of any Indian language, where each word may have thousands and millions of word forms. A significant part of grammar that is handled by syntax in English (and other similar languages) is handled within morphology in Telugu. Phrases including several words (that is, tokens) in English would be mapped on to a single word in Telugu.Telugu language is phonetic in nature in addition to rich in morphology. That is why the speech technology developed for English cannot be applied to Telugu language. This paper highlights the work carried out in an attempt to build a voice enabled text editor with capability of automatic term suggestion. Main claim of the paper is the recognition enhancement process developed by us for suitability of highly inflecting, rich morphological languages. This method results in increased speech recognition accuracy with very much reduction in corpus size. It also adapts Telugu words to the database dynamically, resulting in growth of the corpus.
It's a brief overview of Natural Language Processing using Python module NLTK.The codes for demonstration can be found from the github link given in the references slide.
This paper deals about the sentiment analysis of the Manipuri article. The language is very highly
agglutinative in Nature. The document files are the letters to the editor of few local daily newspapers. The
text is processed for Part of Speech (POS) tagging using Conditional Random Field (CRF). The lexicon of
verbs is modified with the sentiment polarity (Positive or Negative or Neutral) manually. With the POS
tagger the verbs of each sentence are identified and the modified lexicon of verbs is used to notify the
polarity of the sentiment in the sentence. The total number of polarity for each category that is positive,
negative and neutral is counted separately. The highest total of the three is the deciding factor of the
sentiment polarity of the document. The system shows a recall of 72.10%, a precision of 78.14% and a Fmeasure
of 75.00%.
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
Deep Learning for Speech Recognition - Vikrant Singh TomarWithTheBest
Tomar discusses the components of speech recognition, the difference between deep learning for speech and images, system architecture, GMM-HMM based systems, deep neural networks in speech, tandem DNN, and hybrids. There's a lot of exciting stuff to talk about in deep learning communities.
Vikrant Singh Tomar, Founder, Fluent.ai
Marathi Isolated Word Recognition System using MFCC and DTW FeaturesIDES Editor
This paper presents a Marathi database and isolated
Word recognition system based on Mel-frequency cepstral
coefficient (MFCC), and Distance Time Warping (DTW) as
features. For the extraction of the feature, Marathi speech
database has been designed by using the Computerized Speech
Lab. The database consists of the Marathi vowels, isolated
words starting with each vowels and simple Marathi sentences.
Each word has been repeated three times by the 35 speakers.
This paper presents the comparative recognition accuracy of
DTW and MFCC.
An Improved Approach for Word Ambiguity RemovalWaqas Tariq
Word ambiguity removal is a task of removing ambiguity from a word, i.e. correct sense of word is identified from ambiguous sentences. This paper describes a model that uses Part of Speech tagger and three categories for word sense disambiguation (WSD). Human Computer Interaction is very needful to improve interactions between users and computers. For this, the Supervised and Unsupervised methods are combined. The WSD algorithm is used to find the efficient and accurate sense of a word based on domain information. The accuracy of this work is evaluated with the aim of finding best suitable domain of word. Keywords: Human Computer Interaction, Supervised Training, Unsupervised Learning, Word Ambiguity, Word sense disambiguation
Deep Learning in practice : Speech recognition and beyond - MeetupLINAGORA
Retrouvez la présentation de notre Meetup du 27 septembre 2017 présenté par notre collaborateur Abdelwahab HEBA : Deep Learning in practice : Speech recognition and beyond
Big Data and Natural Language ProcessingMichel Bruley
Natural Language Processing (NLP) is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language.
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
It's a brief overview of Natural Language Processing using Python module NLTK.The codes for demonstration can be found from the github link given in the references slide.
This paper deals about the sentiment analysis of the Manipuri article. The language is very highly
agglutinative in Nature. The document files are the letters to the editor of few local daily newspapers. The
text is processed for Part of Speech (POS) tagging using Conditional Random Field (CRF). The lexicon of
verbs is modified with the sentiment polarity (Positive or Negative or Neutral) manually. With the POS
tagger the verbs of each sentence are identified and the modified lexicon of verbs is used to notify the
polarity of the sentiment in the sentence. The total number of polarity for each category that is positive,
negative and neutral is counted separately. The highest total of the three is the deciding factor of the
sentiment polarity of the document. The system shows a recall of 72.10%, a precision of 78.14% and a Fmeasure
of 75.00%.
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
Deep Learning for Speech Recognition - Vikrant Singh TomarWithTheBest
Tomar discusses the components of speech recognition, the difference between deep learning for speech and images, system architecture, GMM-HMM based systems, deep neural networks in speech, tandem DNN, and hybrids. There's a lot of exciting stuff to talk about in deep learning communities.
Vikrant Singh Tomar, Founder, Fluent.ai
Marathi Isolated Word Recognition System using MFCC and DTW FeaturesIDES Editor
This paper presents a Marathi database and isolated
Word recognition system based on Mel-frequency cepstral
coefficient (MFCC), and Distance Time Warping (DTW) as
features. For the extraction of the feature, Marathi speech
database has been designed by using the Computerized Speech
Lab. The database consists of the Marathi vowels, isolated
words starting with each vowels and simple Marathi sentences.
Each word has been repeated three times by the 35 speakers.
This paper presents the comparative recognition accuracy of
DTW and MFCC.
An Improved Approach for Word Ambiguity RemovalWaqas Tariq
Word ambiguity removal is a task of removing ambiguity from a word, i.e. correct sense of word is identified from ambiguous sentences. This paper describes a model that uses Part of Speech tagger and three categories for word sense disambiguation (WSD). Human Computer Interaction is very needful to improve interactions between users and computers. For this, the Supervised and Unsupervised methods are combined. The WSD algorithm is used to find the efficient and accurate sense of a word based on domain information. The accuracy of this work is evaluated with the aim of finding best suitable domain of word. Keywords: Human Computer Interaction, Supervised Training, Unsupervised Learning, Word Ambiguity, Word sense disambiguation
Deep Learning in practice : Speech recognition and beyond - MeetupLINAGORA
Retrouvez la présentation de notre Meetup du 27 septembre 2017 présenté par notre collaborateur Abdelwahab HEBA : Deep Learning in practice : Speech recognition and beyond
Big Data and Natural Language ProcessingMichel Bruley
Natural Language Processing (NLP) is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language.
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
Signal Processing Tool for Emotion Recognitionidescitation
In the course of realization of modern day robots,
which not only perform tasks, but also behaves like human
beings during their interaction with the natural environment,
it is essential for us to impart knowledge of the underlying
emotions in the spoken utterances of human beings to the
robots, enabling them to be consistent, whole, complete and
perfect. To this end, it is essential for them too to understand
and identify the human emotions. For this reason, stress is
laid now-a-days on the study of emotional content of the speech
and accordingly speech emotion recognition engines have been
proposed. This paper is a survey of the main aspects of speech
emotion recognition, namely, features extractions and types
of features commonly used, selection of most informed
features from the original dataset of the features, and
classification of the features according to different classifying
techniques based on relative information regarding commonly
used database for the speech emotion recognition.
Emotion Detection is one of the most emerging issues in human computer interaction. A sufficient amount
of work has been done by researchers to detect emotions from facial and audio information whereas
recognizing emotions from textual data is still a fresh and hot research area. This paper presented a
knowledge based survey on emotion detection based on textual data and the methods used for this purpose.
At the next step paper also proposed a new architecture for recognizing emotions from text document.
Proposed architecture is composed of two main parts, emotion ontology and emotion detector algorithm.
Proposed emotion detector system takes a text document and the emotion ontology as inputs and produces
one of the six emotion classes (i.e. love, joy, anger, sadness, fear and surprise) as the output.
Natural language processing with python and amharic syntax parse tree by dani...Daniel Adenew
Natural Language Processing is an interrelated disincline adding the capability of communicating as human beings to Computerworld. Amharic language is having much improvement over time thanks to researcher at PHD, MSC level at AAU. Here , I have tried to study and come up a limited scope solution that does syntax parsing for Amharic language and draws syntax parse trees using Python!!
A SURVEY OF GRAMMAR CHECKERS FOR NATURAL LANGUAGEScsandit
ABSTRACT
Natural Language processing is an interdisciplinary branch of linguistic and computer science studied under the Artificial Intelligence (AI) that gave birth to an allied area called
‘Computational Linguistic’ which focuses on processing of natural languages on computational devices. A natural language consists of a large number of sentences which are linguistic units involving one or more words linked together in accordance with a set of predefined rules called grammar. Grammar checking is the task of validating sentences syntactically and is a prominent tool within language engineering. Our review draws on the recent development of various grammar checkers to look at past, present and the future in a new light. Our review covers grammar checkers of many languages with the aim of seeking their approaches, methodologies for developing new tool and system as a whole. The survey concludes with the discussion of various features included in existing grammar checkers of foreign languages as well as a few Indian Languages.
Speech is the important mode of communication and is the current research
topic. The concentration is mostly focused on synthesis and analyzing part.Apart of
synthesizing, text to speech system is developed.Speech synthesis is an artificial production
of human speech.A text to speech system (TTS) is to convert an arbitrary text into speech.In
India different languages have been spoken each being the mother tongue of tens of millions
of people.In this paper,the text to speech system is primarily developed for Telugu, a
Dravidian language predominantly spoken in Indian state of Andhra Pradesh.The
important qualities expected from this system are naturalness and intelligibility.Telugu TTS
can be developed using other synthesis methods like articulatory synthesis,formant synthesis
and concatenative synthesis.This paper describes a development of a Telugu text to speech
system using concatenative synthesis method on mobile based system OMAP 3530 (ARM
Cortex A-8 core) in Linux.
Signal & Image Processing : An International Journal sipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
ASERS-LSTM: Arabic Speech Emotion Recognition System Based on LSTM Modelsipij
The swift progress in the study field of human-computer interaction (HCI) causes to increase in the interest in systems for Speech emotion recognition (SER). The speech Emotion Recognition System is the system that can identify the emotional states of human beings from their voice. There are well works in Speech Emotion Recognition for different language but few researches have implemented for Arabic SER systems and that because of the shortage of available Arabic speech emotion databases. The most commonly considered languages for SER is English and other European and Asian languages. Several machine learning-based classifiers that have been used by researchers to distinguish emotional classes: SVMs, RFs, and the KNN algorithm, hidden Markov models (HMMs), MLPs and deep learning. In this paper we propose ASERS-LSTM model for Arabic Speech Emotion Recognition based on LSTM model. We extracted five features from the speech: Mel-Frequency Cepstral Coefficients (MFCC) features, chromagram, Melscaled spectrogram, spectral contrast and tonal centroid features (tonnetz). We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we also construct a DNN for classify the Emotion and compare the accuracy between LSTM and DNN model. For DNN the accuracy is 93.34% and for LSTM is 96.81%
Emotion can be expressed in many ways that can be seen such as facial expression and
gestures, speech and by written text. Emotion Detection in text documents is essentially a
content – based classification problem involving concepts from the domains of Natural
Language Processing as well as Machine Learning. In this paper emotion recognition based on textual data and the techniques used in emotion detection are discussed.
Natural Language Processing: A comprehensive overviewBenjaminlapid1
Natural language processing enhances human-computer interaction by bridging the language gap. Uncover its applications and techniques in this comprehensive overview. Dive in now!
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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.
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
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
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
C5 giruba beulah
1.
2. On Emotion Detection from Tamil Text
Giruba Beulah S E, and Madhan Karky V
Tamil Computing Lab (TaCoLa),
College of Engineering, Anna University, Chennai.
(ljcsegb@gmail.com) (madhankarky@gmail.com)
Abstract
Emotion detection from text is pragmatically complicated than such recognition from audio and
video, as text has no audio or visual cues. Techniques of emotion identification from text are, by and
large, linguistic based, machine learning based or a combination of both. This paper intends to
perceive emotions from Tamil news text, in the perspective of a positive profile, through a neural
network. The chief inputs to the neural network are outputs from a domain classifier, two schmaltzy
analyzers and three affect taggers. To aid precise recognition, tense affect, inanimate/animate case
affect and sub-emotion affect dole out as the supplementary inputs. The emotion thus recognized by
the generalized neural net is displayed via a two dimensional animated face generator. The
performance and evaluation of the neural network are then reported.Index Terms—Emotion
detection, Machine learning, Neural network, Tamil news text.
I. Introduction
Emotion detection from text attracts substantial attention these days as this if realized, could result in
the realization of a lot of fascinating applications like emotive android assistants, blog emotion
animators etc. However, emotion detection from text is not trouble-free, as one cannot, with a single
glance get a hold of the emotions of the people about whom the text is based. Further, what appears to
be sad for a person may perhaps appear fear for someone. Emotion detection from text is thus
influenced by the empathy of the readers. Also, as there may be no background information, to shore
up the emotion of a person in text, it is better if emotion detection is based on some model empathy.
The aim of this paper is to detect emotion from Tamil news text by a self learning neural network
which takes in linguistic and part of speech emotive features. The emotion is identified by assigning
weights for features based on their affective influence.
II. Background
Tamil is a Dravidian language as old as five thousand years and enjoys classical language of the world
status along with Hebrew, Greek, Latin, Chinese and Sanskrit. Unlike Sanskrit, Latin and Greek,
which are very rarely in use, it is a living language and has fathered many Dravidian languages like
Malayalam, Telugu etc. It is morphologically very rich and has a partial free word order. It groups
noun and verb modifiers, (adjectives and adverbs) under a single category, Urichols. Noun participles
are equally affective as the verb participles. Thus, apart from parts of speech, morphological entities
like cases and participles are also affective.
Among the methods of emotion detection, [1] observes that the Support Vector Machines using
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3. manual lexicons and Bag Of Words approach perform better. The Support Vector Regression
Correlation Ensemble is an album of classifiers, each trained using a feature subset tailored to find a
single affect class. However, as support Vector machines suffer from high algorithmic complexity and
requirement of quadratic programming and do not efficiently model non-linear problems, Neural
networks has been opted as they provide greater accuracy than Support Vector Machines.
Nevertheless, Neural networks sport disadvantages like local minima and over fitting. The former can
be handled by adding momentum and the latter is usually solved by early stopping. Since the neural
net could memorize all training examples if over the need hidden neurons are present, three
promising prototypes are constructed, trained and tested to find the optimal one. To this optimal
neural net, the affective feature tags from Tamil news text are given, to recognize the inherent
emotion, based on their respective affective strength.
Kao, Leo, Yahng, Hsieh and Soo use a combinatory approach of dependency trees, emotion model
ontology and Case based reasoning [2] where cases are manually annotated. Such an approach may
work for languages of few cases. However, not all cases in a language may be affect sensitive. Cases in
Tamil are eight and only two of these are affect sensitive, namely the instrumental and the accusative
case. Thus manually annotating cases in Tamil would normally fail as cases aid in increasing or
decreasing the affect of the verb nearby.
The separate sub-class networks [10] for Emotion recognition with context independence in speech
seem to be promising but for the low precision of 50 even when the learning is supervised using a
simple backpropogation network. However, this project is similar to the above in one aspect; in
assuming model empathy to support context independence, thereby avoiding the bias on a particular
individual involved in the text.
Sugimoto and Masahide [9] split the text into discourse units and then into sentences identifying the
emotion of discourse and sentences separately. The language of the text considered is Chinese which
is monosyllabic partially with nouns, verbs and adjectives being largely disyllabic. Tamil is partial free
word order and does not have such phonological restrictions. Hence, application of such an approach
to Tamil may not be fitting intuitively.
Soo Seoul, Joo Kim and Woo Kim propose to use keyword based model for emotion recognition when
keywords are present and to use Knowledge based ANN when text lacked emotional keywords [6].
The KBANN uses horn clauses and example data. However, the accuracy of emotion recognition
using KBANN is less ranging from 45%-63% compared to the recognition range of 90% where
emotional keyword affect analysis is incorporated.
Most of the research in emotion recognition is directed toward audio and video from which one can
infer so many cues even without understanding the narration .For text, the features of the language of
text has a major emphasis on emotion recognition. Tamil is a morphologically rich language and
hence its affect sensitive features would drastically vary with the usually chosen languages for
emotion recognition like Chinese which is character oriented and English which is least partially
inflected.
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4. III. An architecture for Emotion Recognition
This section throws light on each module and its functions. Tamil Morphological analyzer, a product
of the Tamil Computing lab of Anna University is used as the tool to retrieve part of speech like
nouns, verbs, modifiers and cases. The 2D animated face generator is another tool to display the found
emotion via a two dimensional face.
Following is the architecture diagram of the Neural net framework.
Fig 1: The Overall Architecture
A) The Domain Classifier
The Domain Classifier takes in documents which are already classified under five domains namely,
politics, cinema, sports, business and health. Training process involves retrieving nouns and verbs
using the Tamil Morphological Analyzer, calculating file constituent terms’ domain frequencies and
inserting them into the respective domain hash tables with terms as keys and term frequencies as
values. Training reports a document’s domain, based on the highest test domain frequency counter.
The algorithm is as follows
i) Preprocessing:
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5. Let FD represent the set of files in a domain and f be a file such that f є FD. Let T be the bag of words in a
domain without stop words and t є T.
1. ∀f є FD, remove stop words and tokenize
2. Get the nouns and verbs using the Tamil Morphological analyzer.
3. ∀t є T, Ft =∑ ft where ft is the frequency of t in f.
ii) Training:
Let HD represent a domain hash table
∀t є T , insert (t, HD) where t being the key and the value v being
v = Iocc + ∑ fdup(v)
where Iocc is the first occurrence of the term in the domain and fdup(v) is the frequency of its duplicate.
iii) Testing:
Initialise domain frequency counters Pc, Cc, Bc, Sc and Hc. Given a document, remove the stopwords
and tokenise.
Let Tok be the bag of nouns and verbs in the document, retrieved using the Tamil Morphological
Analyser.
Convert Tok to a set by removing the duplicates.
∀t є Tok, get the frequencies of t, from all the domain hash tables. Increment the domain frequency
counter of the domain that yields the highest frequency for t.
Report the domain of the test document as the domain that has the highest counter value.
B) The Negation Scorer
The Negation classifier gets the documents, analyses whether positive words occur in the
neighborhood of negative words or whether likes come close to dislikes and vice versa and assigns a
score.
i) Training:
Let F denote set of all files in the corpus and let f є F. Let T be the bag of words in a file f without
stop words and t є T.
a) ∀f є F, remove stop words and tokenize.
b) ∀f є F, let Neg denote the negation score of f primarily intialised to 1.
c) ∀t є T, let i be the current position.Check whether t is a positive/negative word or like/dislike
word.
if t is positive/like, and a negative/dislike word occurs in a window of three places to the left or
right, calculate Neg as
Neg = Neg – 0.01
if t is negative/dislike, and a negative/like word occurs in a window of three places to the left or
right, calculate Neg as
Neg = Neg – 0.1
d) If Neg < -0.5 ,report the document as negative.
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6. C) The Flow Scorer
The Flow scorer assigns a score to each document based on the pleasantness of the words it has. The
score is set in view of the phonetic classification in Tamil alongside with place and manner of
articulation. Let F denote set of all files in the corpus and let f є F. Let T be the bag of words in a file f
without stop words and t є T. Let tg be the English equivalent of a Tamil word t.
a) ∀f є F, remove stop words and tokenize.
b) ∀t є f,convert t to tg
c) ∀ tg є f, compute the flow score as the sum of the Maaththirai counts diminishing when Kurukkams
appear.
Table 1: Kurukkams and Maaththirai
Rule Context Final Maaththirai
KuttriyaLukaram One among these 1,(Decrease is 0.5)
at the end of the word
Aukaarakkurukkam ஔ in the beginning 1.5,(Decrease is 0.5)
Aikaarakkurukkam ஐ in the beginning and middle 1.5,(Decrease is 0.5)
ஐ in the end 1,(Decrease is 1)
Maharakkurukkam வ before 0.25,(Decrease is 1/4)
Table 2. Phonemes under Manner of articulation
Category Manner of Articulation Phoneme
Greater Rough Retroflex, Trill டற
Rough Tap, Dental, Bilabial கசதபர
Intermediate Semivowels, Approximants யலளழவ
Soft Nasal ஙஞணநமன
Let t be the Tamil word, tg be the Grapheme form and Pt be the bag of phonemes in t with p є Pt. Let
GRscore be the score of the greater rough category, Rscore be the score of the rough category, Iscore be
the intermediate score and Sscore be the score of the soft category.
i) Calculate GRscore, Rscore, Iscore, Sscore as
GRscore = ∑ f(p)GR .
Rscore = ∑ f(p)R .
Iscore = ∑ f(p)I .
Sscore = ∑ f(p)S .
where f(p)GR is the frequency of a greater rough category phoneme , f(p)R is the frequency of a rough
category phoneme, f(p)I is the frequency of a intermediate category phoneme and f(p)S is the frequency
of a soft category phoneme.
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7. ii) Calculate the FinalRoughScore and FinalSoftScore as
FinalRoughScore = GRscore + Rscore .
FinalSoftScore = Iscore + Sscore .
iii) If FinalRoughScore > FinalSoftScore T→ Pleasant
iv) Else if FinalSoftScore > FinalRoughScore T→ Unpleasant
v) Else T→ Neutral
D) The Taggers
At the start four taggers were constructed specifically, the noun tagger, verb tagger, Urichol tagger and
case tagger. But for the instrumental and accusative cases, the left behind cases are not affective. For
this reason, the constructed case tagger is unused as an input to the neural network. Nonetheless, the
affect sensitive cases are incorporated in the affect specificity improver, namely the
animate/inanimate affect handler.
Each tagger, picks up the respective part of speech in the document, analyses the major affect and tags
the document with that affect. As a consequence, noun tagger reports the affect of nouns, verb tagger
reports the affect of verbs and Urichol tagger reports the affect of Urichols. The generalized algorithm is
as follows.
Let F denote set of all files in the corpus and let f є F. Let T be the bag of words in a file f without stop
words and t є T. Let N denote nouns, V denote the verbs and U denote the Urichols in a file. Let n є N,
v є V, u є U.
a) ∀f є F, remove stop words and tokenize.
b) ∀f є F, get N for Noun Tagger, V for Verb tagger and U for Urichol tagger, using the Tamil
Morphological analyzer.
c) ∀n є N, use the noun affect lexicon to determine the noun affects. Initialize respective noun affect
counters for the basic six emotions and increment them depending on the affect of each n.
d) ∀v є V, use the Verb affect lexicon to finalize the verb affects. Initialize respective affect verb
counters for the basic six emotions and increment them depending on the affect of each v.
e) ∀u є U, find the Urichol affect using the Urichol affect lexicon. Initialise respective affect counters
for the basic six emotions and increment them depending on the affect of each u.
f) For each tagger, report the final affect of a file f, as the affect of the respective affect counter that has
the maximum value.
E) The Tense Affect Handler
a) ∀f є F, get the verbs under each of the three tenses. Let Pr represent the present tense, Pa denote the
past tense and F denote the future tense.
b) Analyze the affect of each tense category verbs.
c) Prioritize Present, Future and then Past tenses.
d) Report the high frequent affect of the Present tense. If present tense verbs are absent, report the
maximum frequent affect of future tense, else report the high frequent affect category of the past tense.
F) The Inanimate/Animate Handler
a) ∀f є F, get the verbs using the Morphological analyzer.
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8. b) Restrict a window size of one to the left to find whether affect sensitive cases are present.
c) Analyze the affect of the verbs and categorize them under mild and dangerous.
d) Analyze the cases and see if they add to the affect of the verbs or nullify it.
e) If affect intensity is increased report danger, else report the affect implied.
G) The Contrary Affect Handler
a) ∀f є F, check whether there are multiple entities in a sentence.
b) Using the Profile monitor, check whether the entities are in like list or dislike list.
c) Analyze the nouns next to the entities to see whether they are favorable to the preferred entities or
not.
d) Report the affect as the affect of the nouns with reference to the preferred entity.
H) The Sub-emotion Spotter
a) ∀f є F, Get all nouns, verbs and urichols using the Tamil morphological analyzer.
b) Use the constructed sub-emotive affect lexicon to identify the high frequent sub emotion.
c) Report the maximum occurring affect based on the sub- affect.
I) The Neural Network
Initially a supervised Backpropogation network was constructed with the architecture 6:3:1.
However, since emotion recognition is to do with emotional intelligence, unsupervised
learning was preferred and Hebbian learning was incorporated. The forget factor is fixed as
0.02 and the learning factor as 0.1. Following is the pseudo code.
a) ∀f є F, get the outputs from Domain Classifier, Negation Scorer, Flow Scorer and the three affect
taggers.(The Improver modules fail to aid emotion recognition by getting eclipsed toward certain
domains and are hence overlooked.)
b) Initialize the network weights and threshold values which are set in the range of 0 to 1.
c) Start the learning for each file using Hebb’s rule.
d) Report the affect as the affect of the output activation that approximately equals an affect value.
e) Stop the learning when steady state is embarked or sufficient number of epochs has been elapsed.
IV. Results and Evaluation
Both Batch and Sequential learning are supported. The precision of the Neural network is 60% . Other
datasets used were lyrics and stories. Stories usually have sub plots and hence overall affect usually
gets eclipsed towards neutral. Hence, news (400 files) and lyrics (230) were used for training.
Validation set included 50 news files with equal contribution from five domains and 10 lyrics.
For lyrics and stories, the neural net has a better precision of 70%. The precision gets increased when
epochs increase for all the datasets. Simple supervised Back propogation network was implemented
and used as the Baseline whose precision was 30% more than the constructed even with minimum
epochs.
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9. Following is a graph that depicts how precision of emotion found varies with respect to the number of
epochs, in the case of a single news text. Values nearer to zero indicate the drastic variation of the
reported affect from the actual one (ie joy instead of sad) and values near 100 indicate the closeness
towards the actual affect.
Fig 2: Epochs vs Precision of affect of a document.
The neural network suffers from ambiguity problems in the case of closely related categories like love-
joy and sad-fear irrespective of dataset. The supervised Back propogation network is used as the
baseline which is 30% more in precision than the Unsupervised Hebbian Learning.
Besides, the Unsupervised Hebbian learning Neural net has exponential complexity and consumes
two thirds of the physical memory. The affect reporting of a single file amounts to three minutes
where approximately one and a half minute is taken for indexing and loading of the domain hash
tables by Domain Classifier. The CPU usage during the learning varies roughly from 11% to 72%.
V. Conclusion
Thus, an unsupervised Hebbian learning neural network is constructed which fetches its major inputs
from a domain classifier, two sentimental scorers and three part of speech taggers to figure out the
affect in the presented text. As is the case with almost all natural language processing applications,
ambiguities do exist in emotion recognition; here among closely related affective categories like love-
joy and sad-fear. However, a competitive strategy in unsupervised learning can be opted to resolve
this issue, as such a learning is concerned with demarcating one from the rest. Identification of other
affect sensitive features could further aid in precise emotion detection.
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