Querying Elasticsearch with Deep Learning to Answer Natural Language QuestionsSebastian Blank
Natural language is gaining more and more relevance as an interface between man and machine. Already today, we are able to carry out simple task by talking to our smartphone or smart speaker, like Google Home or Alexa. An important challenge for any kind of dialog agent or chatbot is to include external knowledge into the conversation with the user. Therefore, such systems need to be able to interact with resources like relational databases or unstructured resources, like search engines. However, the complexity of natural language makes it hard to capture diverse utterances with a set pre-defined rules. Instead, we present an approach that leverages Deep Learning to learn how to query an Elasticsearch given natural language questions. As our model learns to follow the inherent logic of querying, it is even possible to switch to other systems and query languages. This carries a great potential for future applications of Elasticsearch and related NoSQL solutions.
I summarized the GPT models in this slide and compared the GPT1, GPT2, and GPT3.
GPT means Generative Pre-Training of a language model and was implemented based on the decoder structure of the transformer model.
(24th May, 2021)
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.
( ** Python Certification Training: https://www.edureka.co/python ** )
This Edureka PPT on Advanced Python tutorial covers all the important aspects of using Python for advanced use-cases and purposes. It establishes all of the concepts like system programming , shell programming, pipes and forking to show how wide of a spectrum Python offers to the developers.
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
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LinkedIn: https://www.linkedin.com/company/edureka
Querying Elasticsearch with Deep Learning to Answer Natural Language QuestionsSebastian Blank
Natural language is gaining more and more relevance as an interface between man and machine. Already today, we are able to carry out simple task by talking to our smartphone or smart speaker, like Google Home or Alexa. An important challenge for any kind of dialog agent or chatbot is to include external knowledge into the conversation with the user. Therefore, such systems need to be able to interact with resources like relational databases or unstructured resources, like search engines. However, the complexity of natural language makes it hard to capture diverse utterances with a set pre-defined rules. Instead, we present an approach that leverages Deep Learning to learn how to query an Elasticsearch given natural language questions. As our model learns to follow the inherent logic of querying, it is even possible to switch to other systems and query languages. This carries a great potential for future applications of Elasticsearch and related NoSQL solutions.
I summarized the GPT models in this slide and compared the GPT1, GPT2, and GPT3.
GPT means Generative Pre-Training of a language model and was implemented based on the decoder structure of the transformer model.
(24th May, 2021)
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.
( ** Python Certification Training: https://www.edureka.co/python ** )
This Edureka PPT on Advanced Python tutorial covers all the important aspects of using Python for advanced use-cases and purposes. It establishes all of the concepts like system programming , shell programming, pipes and forking to show how wide of a spectrum Python offers to the developers.
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
This presentation I initially presented at Data Science UA meetup in August, 2018. Link to the video: https://www.youtube.com/watch?v=Ksg_36ljcQ8&feature=youtu.be&app=desktop&fbclid=IwAR0YQ_WR2YlBLrLSCcLWmV2WviVF1Eo4KB6YCu7C5HNCpCrhEwO-1AIbGqE.
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 presentation goes into the details of word embeddings, applications, learning word embeddings through shallow neural network , Continuous Bag of Words Model.
Information Extraction, Named Entity Recognition, NER, text analytics, text mining, e-discovery, unstructured data, structured data, calendaring, standard evaluation per entity, standard evaluation per token, sequence classifier, sequence labeling, word shapes, semantic analysis in language technology
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.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
This presentation I initially presented at Data Science UA meetup in August, 2018. Link to the video: https://www.youtube.com/watch?v=Ksg_36ljcQ8&feature=youtu.be&app=desktop&fbclid=IwAR0YQ_WR2YlBLrLSCcLWmV2WviVF1Eo4KB6YCu7C5HNCpCrhEwO-1AIbGqE.
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 presentation goes into the details of word embeddings, applications, learning word embeddings through shallow neural network , Continuous Bag of Words Model.
Information Extraction, Named Entity Recognition, NER, text analytics, text mining, e-discovery, unstructured data, structured data, calendaring, standard evaluation per entity, standard evaluation per token, sequence classifier, sequence labeling, word shapes, semantic analysis in language technology
6. 中文斷詞演算法
● 監督式學習法:
B :開頭
M :中間
E :結尾
S :單詞
馬英九說
經濟政策
造成景氣
回升 ...
馬B
英M
九E
說 S
經 B
濟 E
政 B
策 E
造 B
…..
語言
模型
手動標記
訓練
語言
模型
馬英九總
統距離卸
任剩下不
到一年 ...
馬 B
英 M
九 E
總 B
統 E
距 B
離 E
卸 B
任 E
剩 B
下 E
...
19. jieba 斷詞
● 斷詞原理:
– 辭典內沒有的詞,用監督式學習法來斷詞
– Ex: →內湖石內卜 內湖 / 石內卜
P(B|<s>) * P( 內 |B) * P(E| B) * P( 湖 |E) * P(B|E) *
P( 石 |B) *P(M|B)* P( 內 |M)* P(E|M)*P( 卜 |E) * P(<e>|E)
B E B M E
內 湖 石 內 卜
Start <s> End <e>
20. 內 湖 石 內 卜
S
B
M
E
Q1
(S) = P( 內 |S) * P(S|<s>)
Q1
(B) = P( 內 |B) * P(B|<s>)
Q1
(M) =P( 內 |M) * P(M|<s>)
Q1
(E) = P( 內 |E) * P(E|<s>)
<s>
21. 內 湖 石 內 卜
SS
B
M
E
Q2
(S) = P( 湖 |S)* MAX( )
P(S|S) Q1
(S)
P(S|B) Q1
(B)
P(S|M) Q1
(M)
P(S|E) Q1
(E)<s>
22. 內 湖 石 內 卜
S
B
M
E
S
B
M
E
Q2
(S) = P( 湖 |S) * P(S|B) * Q1
(B)
Q2
(B) = P( 湖 |B) * P(B|S) * Q1
(S)
Q2
(M) = P( 湖 |M) * P(M|B) * Q1
(B)
Q2
(E) = P( 湖 |E) * P(E|B) * Q1
(B)
<s>
23. 內 湖 石 內 卜
S
B
M
E
S
B
M
E
S
Q3
(S) = P( 石 |S)* MAX( )
P(S|S) Q2
(S)
P(S|B) Q2
(B)
P(S|M) Q2
(M)
P(S|E) Q2
(E)
<s>
24. 內 湖 石 內 卜
S
B
M
E
S
B
M
E
Q3
(S) = P( 石 |S) * P(S|S) * Q2
(S)
Q3
(B) = P( 石 |B) * P(B|E) * Q2
(E)
Q3
(M) = P( 石 |M) * P(M|B) * Q2
(B)
Q3
(E) = P( 石 |E) * P(E|M) * Q2
(M)
S
B
M
E
<s>
25. 內 湖 石 內 卜
S
B
M
E
S
B
M
E
S
B
M
E
S
B
M
E
S
B
M
E
<s>
<s>
MAX( )
P(<e>|S) Q5
(S)
P(<e>|B) Q5
(B)
P(<e>|M) Q5
(M)
P(<e>|E) Q5
(E)
斷詞結果:內湖 / 石內卜