Word embedding, Vector space model, language modelling, Neural language model, Word2Vec, GloVe, Fasttext, ELMo, BERT, distilBER, roBERTa, sBERT, Transformer, Attention
General background and conceptual explanation of word embeddings (word2vec in particular). Mostly aimed at linguists, but also understandable for non-linguists.
Leiden University, 23 March 2018
A Simple Introduction to Word EmbeddingsBhaskar Mitra
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
This is presentation about what skip-gram and CBOW is in seminar of Natural Language Processing Labs.
- how to make vector of words using skip-gram & CBOW.
Word embedding, Vector space model, language modelling, Neural language model, Word2Vec, GloVe, Fasttext, ELMo, BERT, distilBER, roBERTa, sBERT, Transformer, Attention
General background and conceptual explanation of word embeddings (word2vec in particular). Mostly aimed at linguists, but also understandable for non-linguists.
Leiden University, 23 March 2018
A Simple Introduction to Word EmbeddingsBhaskar Mitra
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
This is presentation about what skip-gram and CBOW is in seminar of Natural Language Processing Labs.
- how to make vector of words using skip-gram & CBOW.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
A Review of Deep Contextualized Word Representations (Peters+, 2018)Shuntaro Yada
A brief review of the paper:
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. In NAACL-HLT (pp. 2227–2237)
This presentation goes into the details of word embeddings, applications, learning word embeddings through shallow neural network , Continuous Bag of Words Model.
Continuous representations of words and documents, which is recently referred to as Word Embeddings, have recently demonstrated large advancements in many of the Natural language processing tasks.
In this presentation we will provide an introduction to the most common methods of learning these representations. As well as previous methods in building these representations before the recent advances in deep learning, such as dimensionality reduction on the word co-occurrence matrix.
Moreover, we will present the continuous bag of word model (CBOW), one of the most successful models for word embeddings and one of the core models in word2vec, and in brief a glance of many other models of building representations for other tasks such as knowledge base embeddings.
Finally, we will motivate the potential of using such embeddings for many tasks that could be of importance for the group, such as semantic similarity, document clustering and retrieval.
BERT: Bidirectional Encoder Representations from TransformersLiangqun Lu
BERT was developed by Google AI Language and came out Oct. 2018. It has achieved the best performance in many NLP tasks. So if you are interested in NLP, studying BERT is a good way to go.
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.
An introduction to the Transformers architecture and BERTSuman Debnath
The transformer is one of the most popular state-of-the-art deep (SOTA) learning architectures that is mostly used for natural language processing (NLP) tasks. Ever since the advent of the transformer, it has replaced RNN and LSTM for various tasks. The transformer also created a major breakthrough in the field of NLP and also paved the way for new revolutionary architectures such as BERT.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
A Review of Deep Contextualized Word Representations (Peters+, 2018)Shuntaro Yada
A brief review of the paper:
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. In NAACL-HLT (pp. 2227–2237)
This presentation goes into the details of word embeddings, applications, learning word embeddings through shallow neural network , Continuous Bag of Words Model.
Continuous representations of words and documents, which is recently referred to as Word Embeddings, have recently demonstrated large advancements in many of the Natural language processing tasks.
In this presentation we will provide an introduction to the most common methods of learning these representations. As well as previous methods in building these representations before the recent advances in deep learning, such as dimensionality reduction on the word co-occurrence matrix.
Moreover, we will present the continuous bag of word model (CBOW), one of the most successful models for word embeddings and one of the core models in word2vec, and in brief a glance of many other models of building representations for other tasks such as knowledge base embeddings.
Finally, we will motivate the potential of using such embeddings for many tasks that could be of importance for the group, such as semantic similarity, document clustering and retrieval.
BERT: Bidirectional Encoder Representations from TransformersLiangqun Lu
BERT was developed by Google AI Language and came out Oct. 2018. It has achieved the best performance in many NLP tasks. So if you are interested in NLP, studying BERT is a good way to go.
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.
An introduction to the Transformers architecture and BERTSuman Debnath
The transformer is one of the most popular state-of-the-art deep (SOTA) learning architectures that is mostly used for natural language processing (NLP) tasks. Ever since the advent of the transformer, it has replaced RNN and LSTM for various tasks. The transformer also created a major breakthrough in the field of NLP and also paved the way for new revolutionary architectures such as BERT.
Context-based movie search using doc2vec, word2vecJIN KYU CHANG
Context-based movie search for user questions that ask the title of the movie using doc2vec, word2vec algorithm.
doc2vec, word2vec 알고리즘을 활용하여 제목이 기억이 나지 않는 영화를 찾는 질문의 문맥을 이용하여 원하는 영화를 찾아주는 내용입니다.
Word2Vec model to generate synonyms on the fly in Apache Lucene.pdfSease
f you want to expand your query/documents with synonyms in Apache Lucene, you need to have a predefined file containing the list of terms that share the same semantic. It’s not always easy to find a list of basic synonyms for a language and, even if you find it, this doesn’t necessarily match with your contextual domain.
The term “daemon” in the domain of operating system articles is not a synonym of “devil” but it’s closer to the term “process”.
Word2Vec is a two-layer neural network that takes as input a text and outputs a vector representation for each word in the dictionary. Two words with similar meanings are identified with two vectors close to each other.
TEXTS CLASSIFICATION WITH THE USAGE OF NEURAL NETWORK BASED ON THE WORD2VEC’S...ijsc
Assigning the submitted text to one of the predetermined categories is required when dealing with
application-oriented texts. There are many different approaches to solving this problem, including using
neural network algorithms. This article explores using neural networks to sort news articles based on their
category. Two word vectorization algorithms are being used — The Bag of Words (BOW) and the
word2vec distributive semantic model. For this work the BOW model was applied to the FNN, whereas the
word2vec model was applied to CNN. We have measured the accuracy of the classification when applying
these methods for ad texts datasets. The experimental results have shown that both of the models show us
quite the comparable accuracy. However, the word2vec encoding used for CNN showed more relevant
results, regarding to the texts semantics. Moreover, the trained CNN, based on the word2vec architecture,
has produced a compact feature map on its last convolutional layer, which can then be used in the future
text representation. I.e. Using CNN as a text encoder and for learning transfer.
Texts Classification with the usage of Neural Network based on the Word2vec’s...ijsc
Assigning the submitted text to one of the predetermined categories is required when dealing with application-oriented texts. There are many different approaches to solving this problem, including using neural network algorithms. This article explores using neural networks to sort news articles based on their category. Two word vectorization algorithms are being used — The Bag of Words (BOW) and the
word2vec distributive semantic model. For this work the BOW model was applied to the FNN, whereas the word2vec model was applied to CNN. We have measured the accuracy of the classification when applying these methods for ad texts datasets. The experimental results have shown that both of the models show us quite the comparable accuracy. However, the word2vec encoding used for CNN showed more relevant results, regarding to the texts semantics. Moreover, the trained CNN, based on the word2vec architecture, has produced a compact feature map on its last convolutional layer, which can then be used in the future text representation. I.e. Using CNN as a text encoder and for learning transfer.
DELAB - sequence generation seminar
Title
Open vocabulary problem
Table of contents
1. Open vocabulary problem
1-1. Open vocabulary problem
1-2. Ignore rare words
1-3. Approximative Softmax
1-4. Back-off Models
1-5. Character-level model
2. Solution1: Byte Pair Encoding(BPE)
3. Solution2: WordPieceModel(WPM)
Word2vec on the italian language: first experimentsVincenzo Lomonaco
Word2vec model and application by Mikolov et al. have attracted a great amount of attention in recent years. The vector representations of words learned by word2vec models have been proven to be able to carry semantic meanings and are useful in various NLP tasks. In this work I try to reproduce the previously obtained results for the English language and to explore the possibility of doing the same for the Italian language.
Deep Learning勉強会@小町研 "Learning Character-level Representations for Part-of-Sp...Yuki Tomo
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を紹介しました。
TEXT ADVERTISEMENTS ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKSijdms
In this paper, we describe the developed model of the Convolutional Neural Networks CNN to a
classification of advertisements. The developed method has been tested on both texts (Arabic and Slovak
texts).The advertisements are chosen on a classified advertisements websites as short texts. We evolved a
modified model of the CNN, we have implemented it and developed next modifications. We studied their
influence on the performing activity of the proposed network. The result is a functional model of the
network and its implementation in Java and Python. And analysis of model results using different
parameters for the network and input data. The results on experiments data show that the developed model
of CNN is useful in the domains of Arabic and Slovak short texts, mainly for some classification of
advertisements. This paper gives complete guidelines for authors submitting papers for the AIRCC
Journals.
(Paper Seminar detailed version) BART: Denoising Sequence-to-Sequence Pre-tra...hyunyoung Lee
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Paper seminar of Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs in 2019 fall semester in Advanced Information Security class(2019.10.24).
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This is presentation of 2019 the 6th IEEE International Conference on Big data and Smart Computing(ASC(the 3rd International Workshop on Affective and Sentimental Computing) of IEEE BigComp 2019), Feb. 2019. (2019. 02. 27)
Natural language processing open seminar For Tensorflow usagehyunyoung Lee
This is presentation for Natural Language Processing open seminar in Kookmin University.
The open seminar reference : https://cafe.naver.com/nlpk
My presentation about how to use tensorflow for NLP open seminar for newbies for tensorflow.
large-scale and language-oblivious code authorship identificationhyunyoung Lee
Paper seminar of Large-Scale and Language-Oblivious Code Authorship Identification in 2018 2 semester in Advanced Topics in Computer Science class(2018.11.06).
This is presentation to inform how to use NLTK(Natural Language Processing Toolkit) with NLTK book's simple examples in Information Retrieval and Data mining class as TA(2017.11.28).
This presentation shows you how to use SVM light and SVM multiclass to classify some feature vector, and how you make input file to classify with those tools in Information Retrieval and Data mining class as TA(2017.11.16).
Accelerate Enterprise Software Engineering with PlatformlessWSO2
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Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
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Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
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Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
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The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
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As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
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Venez le découvrir lors de cette session ignite
2. Agenda
1. Word Embedding
- Vectorization of Image and Text
Word2Vec
2. Word2Vec
- One-hot vector and Co-occurrence matrix for word vector
3. Fundamental
- Basic component of word embedding in a neural net
4. Word Vector in a neural net
5. Word2Vec, CBOW and skip-gram
- Comparing Image processing with Word Vector about vector presentation.
6. Glove
3. - Image Vector representation
1. Word Embedding Word2Vec
- RGB Values of every pixel like Height * Width * RGB as a value in a row
So it is easy to make the image a vector in some space, i.e. RGB space.
4. - What is the Word Embedding ?
1. Word Embedding Word2Vec
- In NLP tasks, Before a neural net,
Word vector is represented by Word frequency like TF-IDF and so on.
In a neural net, There are multiple tries for word vector representation :
- Language modeling and Word embedding modeling
5. One-hot representation
Dim = |V| (v is the size of vocabulary)
- motel
- hotel
If you search for [Seattle motel] key word, we want the search engine to match web page containing
“Seattle hotel”
Similarity(motal, hotel) = 0
motel
hotel = 0
If we do inner product with the above vectors, we can not find out similarity between words
2. Word2Vec Word2Vec
T
6. Co-occurrence matrix
Let’s see window based co-occurrence matrix
- Example Corpus :
- I like deep learning.
- I like NLP.
- I enjoy flying.
Total vocabulary size(|V|) = 8
Vector(“I”) = [0, 2, 1, 0, 0, 0, 0, 0]
Vector(“like”) = [2, 0, 0, 1, 0, 1, 0 , 0] …
2. Word2Vec Word2Vec
7. Co-occurrence with SVD
With SVD(Singular Value Decomposition)
- this calculation is so expensive and not efficient. For example, for M * N matrix is O(mn )
- SVD based methods don’t scale well for big matrices, and it is too hard to incorporate new words
or documents
2. Word2Vec Word2Vec
2
8. 3. Fundamental Word2Vec
output layer’s values is regarded as :
- score
- probability
Backpropagation makes those value maximum or
minimum
Feedforward Neural Network(Basic Neural Network)
9. - Embedding Layer(Inner product)
3. Fundamental Word2Vec
- Intermediate Layer(s)
- Softmax Layer
- One or more layer that produce an intermediate representation of the input
For Example, Hidden layer with tanh, sigmoid activation function or RNN(LSTM, GRU) which is
state-of-the-art neural language models.
- The final layer to compute the probability distribution over words in total vocabulary.
10. Language model and Word embedding model with a neural net
- The main purpose of language model is to compute the probability of a sentence or sequence of
words and the probability of an upcoming word
The probability of a sequence of m words {W1, … , Wm} is denoted as P(W1, … , Wm)
P(W1, … , Wm) is conditioned on a widow of n previous words : P(Wt | Wt-1 , … , Wt-n+1)
i.e. The probability of a sentence or sequence of words :
The probability of an upcoming word :
- So, a model that computes either of those probability above is called a language model(LM)
- The Chain Rule applied to compute joint probability of words in sentence
Markov Assumption :
for example, P(“it water is so transparent”) =
P(its) * P(water | its) * P(is | its water) * P(so | its water is) * P(transparent | its water is so)
By Markov Assumption, the probability of the above sentence :
OR
4. Word Vector in a neural net Word2Vec
11. Language model and Word embedding model with a neural net
- How to estimate these probability
In N-gram based language model -
For example, bigram -
trigram -
4. Word Vector in a neural net Word2Vec
12. Language model and Word embedding model with a neural net
- The first deep neural network architecture model For NLP presented by Bengio et al(2003) to predict
P(Wt | Wt-1 , … , Wt-n+1)
- This model is prototype which we now refer to as a word embedding.
There is some issue :
- softmax layer
- computing power
4. Word Vector in a neural net Word2Vec
Classic neural language model (Bengio et al. 2003)
13. Language model and Word embedding model with a neural net
- A little more model than Begino et al is C&W model(2011)
There is some variation :
- changing cost function like the above
4. Word Vector in a neural net Word2Vec
The C&W model without ranking objective(collobert et al. 2011)
14. Language model and Word embedding model with a neural net
- Another way to make word2vec in a neural net
- In NLP, transfer learning is word2vec, BUT Sometimes
we could make word2vec on the specific task using a neural net
4. Word Vector in a neural net Word2Vec
15. Distributional similarity based representations
A lot of value by presenting a word by means of its neighbors
One of the most successful ideas of modern statistical NLP
5. Word2Vec, CBOW, skip-gram Word2Vec
Banking
16. Google’s Word2Vec – CBOW, skip-gram
Goal : simple (shallow) neural network model
Learning from billion words scale corpus
Predict middle word from neighbors with
A fixed size context window
1. Skip-gram
2. CBOW(continuous bag-of-words)
5. Word2Vec, CBOW, skip-gram Word2Vec
17. Skip-gram
Method : Predict neighbor Wt+j given word Wt
Maximizes following average log probability
5. Word2Vec, CBOW, skip-gram Word2Vec
Skip-gram(Mikolov et al. 2013)
18. CBOW
Method : Predict word given bag-of-neighbors
Loss function =
5. Word2Vec, CBOW, skip-gram Word2Vec
CBOW(Mikolov et al. 2013)
19. Skip-gram & CBOW
WV*N (WIN)and W’N*V (WOUT) is embedding layer.
N of these embedding layer is word2vec’s dimension
5. Word2Vec, CBOW, skip-gram Word2Vec
20. Let’s see an example of skip-gram
5. Word2Vec, CBOW, skip-gram Word2Vec
21. Word Analogies with Word2Vec
[king] – [man] + [woman] ≈ [queen]
5. Word2Vec, CBOW, skip-gram Word2Vec
22. Word Analogies with Word2Vec
[king] – [man] + [woman] ≈ [queen]
5. Word2Vec, CBOW, skip-gram Word2Vec
26. Stanford lecture(Online)
CS224n : Natural Language Processing with Deep Learning
- lecture note1 : http://web.stanford.edu/class/cs224n/lecture_notes/cs224n-2017-notes1.pdf
- lecture note2 : http://web.stanford.edu/class/cs224n/lecture_notes/cs224n-2017-notes2.pdf
- lecture note5 : http://web.stanford.edu/class/cs224n/lecture_notes/cs224n-2017-notes5.pdf
- lecture slide2 :http://web.stanford.edu/class/cs224n/lectures/cs224n-2017-lecture2.pdf
CS223n : Convolutional Natural Networks For Visual Recognition
- lecture note : Neural networks Part 1: Setting up the Architecture http://cs231n.github.io/neural-networks-1/
- lecture note : Linear classification : Support Vector Machine, Softmax http://cs231n.github.io/linear-classify/
Sebastian Ruder blog : http://ruder.io/word-embeddings-1/index.html#fn:2
Colah’s blog : http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/
Neural Text Embedding for information Retrieval (WSDM 2017) by MicroSoft
Mikolov, T., Corrado, G., Chen, K., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. Proceedings
of the International Conference on Learning Representations (ICLR 2013), 1–12
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionali
ty. NIPS, 1–9.
Reference Word2Vec
Private Blog
ACM International Conference on Web Search and Data mining
Paper