This report shows what a dependency structure is, why a dependency structure is useful, and how to parse natural sentences to dependency structures. The report describes two stat-of-art dependency parsers, MaltParser and MSTParser, and shows comparisons between the parsers and ways to integrate them. Finally, it suggests a new parsing algorithm and possible applications using dependency structures.
This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification.
(Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Abstractive text summarization is nowadays one of the most important research topics in NLP. However, getting a deep understanding of what it is and also how it works requires a series of base pieces of knowledge that build on top of each other. This is the reason why this presentation will give audiences an overview of sequence-to-sequence with the acceleration of various versions of attention over the past few years. In addition, natural language generation (NLG) with the focusing on decoder techniques and its relevant problems will be reviewed, as a supportive factor to the light of the success of automatic summarization. Finally, the abstractive text summarization will be represented with potential approaches to tackle some hot issues in some latest research papers.
This report shows what a dependency structure is, why a dependency structure is useful, and how to parse natural sentences to dependency structures. The report describes two stat-of-art dependency parsers, MaltParser and MSTParser, and shows comparisons between the parsers and ways to integrate them. Finally, it suggests a new parsing algorithm and possible applications using dependency structures.
This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification.
(Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Abstractive text summarization is nowadays one of the most important research topics in NLP. However, getting a deep understanding of what it is and also how it works requires a series of base pieces of knowledge that build on top of each other. This is the reason why this presentation will give audiences an overview of sequence-to-sequence with the acceleration of various versions of attention over the past few years. In addition, natural language generation (NLG) with the focusing on decoder techniques and its relevant problems will be reviewed, as a supportive factor to the light of the success of automatic summarization. Finally, the abstractive text summarization will be represented with potential approaches to tackle some hot issues in some latest research papers.
