Tensorflow
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The presentation introduces you to Tensorflow, different types of NLP techniques like CBOW and skip-gram and also Jupyter-notebook. It explains the topics through a problem statement where we wanted to cluster the feedbacks from the knolx sessions, basically it takes you through the process of problem-solving with deep learning models.
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* How these approaches perform against each other on a real-world data.
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word in a given sentence. To find of guess the missing word
we have two main methods one of them statistical language
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relation between words and here we use Markov chain. Since
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https://www.youtube.com/watch?v=5ZUlVlumIQo&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=10
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References:
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444.
Socher, Richard, Yoshua Bengio, and Chris Manning. "Deep learning for NLP." Tutorial at Association of Computational Logistics (ACL), 2012, and North American Chapter of the Association of Computational Linguistics (NAACL) (2013).
Lee, Honglak. "Tutorial on deep learning and applications." NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. 2010.
LeCun, Yann, and M. Ranzato. "Deep learning tutorial." Tutorials in International Conference on Machine Learning (ICML’13). 2013.
Socher, Richard, et al. "Recursive deep models for semantic compositionality over a sentiment treebank." Proceedings of the conference on empirical methods in natural language processing (EMNLP). Vol. 1631. 2013.
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
https://www.udacity.com/course/deep-learning--ud730
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
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Like the visual cortex, the regions of the brain involved in understanding language represent information hierarchically. But whereas the visual cortex organizes things into a spatial hierarchy, the language regions encode information into a hierarchy of timescale. This organization is key to our uniquely human ability to integrate semantic information across narratives. More and more, deep learning-based approaches to natural language understanding embrace models that incorporate contextual information at varying timescales. This has not only led to state-of-the art performance on many difficult natural language tasks, but also to breakthroughs in our understanding of brain activity.
In this talk, we will discuss the important connection between language understanding and context at different timescales. We will explore how different deep learning architectures capture timescales in language and how closely their encodings mimic the brain. Along the way, we will uncover some surprising discoveries about what depth does and doesn’t buy you in deep recurrent neural networks. And we’ll describe a new, more flexible way to think about these architectures and ease design space exploration. Finally, we’ll discuss some of the exciting applications made possible by these breakthroughs.
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Summary: For the last 2 month I've been building a system for classifying customer support tickets into several categories in terms of product area, importance, etc. Throughout that time I've tried several approaches and benchmarked them against each other. In this talk I would like to showcase some of my findings, including research algorithms that perform well and relevant software. This talk would be useful for someone who needs to build a text categorization system, or someone who just wants to get an overview of one of the most popular NLP research problems (classification).
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* About various approaches used for text classification (e.g. approaches based on TF-IDF, or approaches based on word embeddings and RNNs - recurrent neural nets).
* How these approaches perform against each other on a real-world data.
* Software that is useful for implementing these approaches.
* Research behind some of these approaches.
http://dataconf.com.ua/speaker-page/volodymyr-lyubinets.php
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Our project is about guessing the correct missing
word in a given sentence. To find of guess the missing word
we have two main methods one of them statistical language
modeling, while the other is neural language models.
Statistical language modeling depend on the frequency of the
relation between words and here we use Markov chain. Since
neural language models uses artificial neural networks which
uses deep learning, here we use BERT which is the state of art
in language modeling provided by google.
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最近のNLP×DeepLearningのベースになっている"Transformer"について、研究室の勉強会用に作成した資料です。参考資料の引用など正確を期したつもりですが、誤りがあれば指摘お願い致します。
This is a material for the lab seminar about "Transformer", which is the base of recent NLP x Deep Learning research.
Learning to understand phrases by embedding the dictionary
The document describes a model that uses an RNN with LSTM cells to learn useful representations of phrases by mapping dictionary definitions to word embeddings, addressing the gap between lexical and phrasal semantics. The model is applied to two tasks: a reverse dictionary/concept finder that takes phrases as input and outputs words, and a general knowledge question answering system for crosswords. The RNN is trained on dictionary definitions to map phrases to target word embeddings, then tested on new input phrases.
Introduction to Transformer Model
Transformer modality is an established architecture in natural language processing that utilizes a framework of self-attention with a deep learning approach.
This presentation was delivered under the mentorship of Mr. Mukunthan Tharmakulasingam (University of Surrey, UK), as a part of the ScholarX program from Sustainable Education Foundation.
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https://www.youtube.com/watch?v=5ZUlVlumIQo&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=10
Over the last years, deep learning is rapidly advancing with impressive results obtained in several areas including computer vision, machine translation and speech recognition. Deep learning attempts to learn complex function through learning hierarchical representation of data. A deep learning model is composed of non-linear modules that each transforms the representation from lower layer to the higher more abstract one. Very complex functions can be learned using enough composition of the non-linear modules. Furthermore, the need for manual feature engineering can be obviated by learning features themselves through the representation learning. In this talk, we first explain how deep learning architecture in particular and neural networks in general are loosely inspired by mammalian visual cortex and nervous system respectively. We also discuss about the reason for big and successful comeback of neural networks with the deep learning models. Finally, we give a brief introduction of various deep structures and their applications to several domains.
References:
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444.
Socher, Richard, Yoshua Bengio, and Chris Manning. "Deep learning for NLP." Tutorial at Association of Computational Logistics (ACL), 2012, and North American Chapter of the Association of Computational Linguistics (NAACL) (2013).
Lee, Honglak. "Tutorial on deep learning and applications." NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. 2010.
LeCun, Yann, and M. Ranzato. "Deep learning tutorial." Tutorials in International Conference on Machine Learning (ICML’13). 2013.
Socher, Richard, et al. "Recursive deep models for semantic compositionality over a sentiment treebank." Proceedings of the conference on empirical methods in natural language processing (EMNLP). Vol. 1631. 2013.
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
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http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
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short story presentation on Framework for Deep Learning-Based Language Models Using Multi-Task Learning in Natural Language Understanding: A Systematic Literature Review and Future Directions
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This document discusses how deep learning techniques can be applied to natural language processing tasks. It begins by explaining some of the limitations of traditional rule-based and machine learning approaches to NLP, such as the lack of semantic understanding and difficulty of feature engineering. Deep learning approaches can learn features automatically from large amounts of unlabeled text and better capture semantic and syntactic relationships between words. Recurrent neural networks are well-suited for NLP because they can model sequential data like text, and convolutional neural networks can learn hierarchical patterns in text.
AINL 2016: Nikolenko
This document provides an overview of deep learning techniques for natural language processing. It begins with an introduction to distributed word representations like word2vec and GloVe. It then discusses methods for generating sentence embeddings, including paragraph vectors and recursive neural networks. Character-level models are presented as an alternative to word embeddings that can handle morphology and out-of-vocabulary words. Finally, some general deep learning approaches for NLP tasks like text generation and word sense disambiguation are briefly outlined.
Transfer_Learning_for_Natural_Language_P_v3_MEAP.pdf
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subrat
This document discusses using natural language processing (NLP) for searching intranets. It begins with an abstract that introduces NLP and the purpose of exploring its use for intranet searching. The introduction provides an overview of NLP, including that it uses tools from artificial intelligence to process natural languages in two ways: parsing and transition networks. The document then discusses the goals, levels, and applications of NLP, and how NLP is implemented through setting up dictionaries and relationships. It concludes that while still a developing area, NLP has shown promise for information access and will continue to be researched and developed for applications like intranet searching.
Tomáš Mikolov - Distributed Representations for NLP
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IRJET- Survey on Deep Learning Approaches for Phrase Structure Identification...
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Suggestion Generation for Specific Erroneous Part in a Sentence using Deep Le...
This document presents a method for generating suggestions for specific erroneous parts of sentences in Indian languages like Malayalam using deep learning. The method uses recurrent neural networks with long short-term memory layers to train a model on input-output examples of sentences and their corrections. The model takes in preprocessed sentence data and generates a set of possible corrections for erroneous parts through multiple network layers. An analysis of the model shows that it can accurately generate suggestions for word length of three, but requires more data and study to handle the complex morphology and symbols of Malayalam. The performance of the method is limited by the hardware used and it could be improved with a more powerful system and additional training data.
Thomas Wolf "An Introduction to Transfer Learning and Hugging Face"
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NLP using Deep learning
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Natural Language Processing (NLP) techniques are a subset of artificial intelligence (AI) that deals with the interactions between computers and human language. Text generation is an important application of NLP that involves the automatic creation of human-like text. This blog post will explore some of the NLP techniques used for text generation.
Feature Extraction and Analysis of Natural Language Processing for Deep Learn...
This document discusses using deep learning techniques for multi-modal feature extraction. It proposes a multi-modal neural network with independent sub-networks for each data mode. It also discusses using a bi-directional GRU network for English word segmentation to effectively solve long-distance dependency issues while reducing training and prediction time compared to bi-directional LSTM. Experimental results showed the proposed multi-modal fusion model can effectively extract low-dimensional fused features from original high-dimensional multi-modal data.
"Large-Scale Deep Learning for Building Intelligent Computer Systems," a Keyn...
"Large-Scale Deep Learning for Building Intelligent Computer Systems," a Keyn...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-google-keynote
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Jeff Dean, Senior Fellow at Google, presents the "Large-Scale Deep Learning for Building Intelligent Computer Systems" keynote at the May 2016 Embedded Vision Summit.
Over the past few years, Google has built two generations of large-scale computer systems for training neural networks, and then applied these systems to a wide variety of research problems that have traditionally been very difficult for computers. Google has released its second generation system, TensorFlow, as an open source project, and is now collaborating with a growing community on improving and extending its functionality. Using TensorFlow, Google's research group has made significant improvements in the state-of-the-art in many areas, and dozens of different groups at Google use it to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, language translation, and many other tasks.
In this talk, Jeff highlights some of ways that Google trains large models quickly on large datasets, and discusses different approaches for deploying machine learning models in environments ranging from large datacenters to mobile devices. He will then discuss ways in which Google has applied this work to a variety of problems in Google's products, usually in close collaboration with other teams. This talk describes joint work with many people at Google.Dealing with Data Scarcity in Natural Language Processing - Belgium NLP Meetup
It’s often said we live in the age of big data. Therefore, it may come as a surprise that in the field of natural language processing, machine learning professionals are often faced with data scarcity. Many organizations that would like to apply NLP lack a sufficiently large collection of labeled text in their language or domain to train a high-quality NLP model.
Luckily, there’s a wide variety of ways to address this challenge. First, approaches such as active learning reduce the number of training instances that have to be labeled in order to build a high-quality NLP model. Second, techniques such as distant supervision and proxy-label approaches can help label training examples automatically. Finally, recent developments in semisupervised learning, transfer learning, and multitask learning help models improve by making better use of unlabeled data or training them on several tasks at the same time.
Artificial inteIegence & Machine learning - Key Concepts
Python is used for development with frameworks like Django and Flask, automation with libraries like subprocess and requests, and data science/ML with libraries like NumPy, Pandas, and Matplotlib. Artificial intelligence involves simulating human intelligence with machines through talking, thinking, learning, planning, and understanding. There are different types of AI like narrow AI that performs specific tasks and general AI that aims for human-level intelligence. Machine learning is a subset of AI that uses algorithms to learn from data without explicit programming, while deep learning uses neural networks inspired by the human brain. Natural language processing gives computers the ability to understand, generate, and interact with human language through techniques like text normalization, tokenization, part-of-speech tagging, text
BIng NLP Expert - Dl summer-school-2017.-jianfeng-gao.v2
This document provides an outline for a tutorial on deep learning for natural language processing. It begins with an introduction to deep learning and its history, then discusses how neural methods have become prominent in natural language processing. The rest of the tutorial is outlined covering deep semantic models for text, recurrent neural networks for text generation, neural question answering models, and deep reinforcement learning for dialog systems.
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Engineering Intelligent NLP Applications Using Deep Learning – Part 2
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IRJET- Survey on Deep Learning Approaches for Phrase Structure Identification...
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Tensorflow
- 1. Tensorflow …. for Deep Learning and NLP Pranjut Gogoi Knoldus Software LLP
- 2. Agenda ● Introduction to Tensorflow ● Introduction to Deep learning ● Introduction to NLP ● Problem Solving
- 3. Problem statement: Find suitable label for each knolx feedback. Challenge: All feedbacks are in text format so normal clustering algorithms won’t be applied.
- 4. Clustering is the task of dividing the data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. A quick introduction to clustering
- 5. Introduction to NLP What is NLP? Wiki: Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
- 6. How NLP works ● Read text written in our human languages ● Interpret its meaning through its own complex filters, and ● Translate it back to us providing exactly what we asked for
- 7. Introduction to NLP Terminologies ● Corpus ● Word Embeddings Models ● Word2Vec ○ CBOW ○ Skip-Gram ● GloVe
- 8. CBOW Context words -> target word ● Build the corpus vocabulary ● Build a CBOW (context, target) generator ● Build the CBOW model architecture ● Train the Model ● Get Word Embeddings
- 9. Skip-gram Target word -> context ● Build the corpus vocabulary ● Build a skip-gram [(target, context), relevancy] generator ● Build the skip-gram model architecture ● Train the Model ● Get Word Embeddings
- 10. GloVe A well-known model that learns vectors or words from their co-occurrence information is GlobalVectors (GloVe). While word2vec is a predictive model—a feed-forward neural network that learns vectors to improve the predictive ability, GloVe is a count-based model. ● Count-based models learn vectors by doing dimensionality reduction on a co-occurrence counts matrix. ● Compared to word2vec, GloVe allows for parallel implementation, which means that it’s easier to train over more data.
- 11. Introduction to Deeplearning ● Neural Network ● Fully Connected Layer ● Convolutional Layer ● CNN ● RNN
- 12. Neural Network An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.
- 13. Fully connected layer Feedforward network
- 14. Fully connected vs Convolutional Layer
- 15. CNN
- 16. RNN
- 17. Introduction to Tensorflow ● What is Tensorflow ● Architecture of Tensorflow ● Estimators ● Keras
- 20. Estimators
- 21. Keras ● User friendly ● Keras has a simple, consistent interface optimized for common use cases. It provides clear and actionable feedback for user errors. ● Modular and composable ● Keras models are made by connecting configurable building blocks together, with few restrictions. ● Easy to extend ● Write custom building blocks to express new ideas for research. Create new layers, loss functions, and develop state-of-the-art models.
- 22. The Solution ● Corpus - KnolxFeedback ● Model - GloVe ● Clustering - K-means clustering ● Tool - Keras and Tensorflow
- 23. Thank you