I'm new to NLP and text mining and just heard about Zipf's Law. I've somewhat understood its explanation through the Wikipedia page on the topic.
Can anyone explain to me with a simple graph/example/code what is happening.
You can find a definition in the context of information retrieval and a nice graph in the IR book.
Related
I am working on a graduation project related to "Aspect extraction (AE)".
I'm pretty confused about POS taging, syntax tree, grammar rules, and other low-level NLP stuff. I need a reference that teaches me these things in detail, so if any of you know I hope you don't mind me?
I know my question is not about programming directly and this may not agree with the site, but I really need to.
This is not an easy one! I assume that you are trying to understand the underlying 'why' and 'what', so, if I were you I would start with the one and only "Speech and Language Processing" by Daniel Jurafsky and James H. Martin. They have a whole section (Section 17 in my Second edition) on the representation of meaning, and state representation, with a whole subsection on Aspect.
In addition to that, the book will also help you understand various existing approaches to POS-tagging and the other topics you mentioned above, and, the book is available online for free! There is even the draft of the 3rd edition out there.
Additionally, after reading the chapters above, you can check out how other people do aspect extraction here
Im trying to understand PLSA (probabilistic latent semantic analysis), to do text modeling (NLP), the problem in every article i red, it's only maths (probabilities), without any semi-algorithme or anything to help you understand that, is there any link where i can understand PLSA please ?
The P in PLSA stands for probablistic and hence I am afraid you may not find any article that does not talk about these. The model itself is a probablistic model and some knowledge of joints, conditionals, independence etc are expected. I would recommend https://medium.com/nanonets/topic-modeling-with-lsa-psla-lda-and-lda2vec-555ff65b0b05 which I found to be the best online resource. There is a bit of Math but most of it is explained well. About PLSA algorithm - I am not sure. It is not used that often and one almost always prefers LDA. I could find a GitHub implementation of solving PLSA using EM here: https://github.com/laserwave/plsa.
I have an UML exam coming up in a few days and have a feeling one of these topics will come up in the form of: "Explain using an example the Expert pattern".
A brief and understandable explanation is all I'm looking for - I haven't been able to find this online anywhere.
Thanks
Information expert is a GRASP pattern, GRASP patterns are related to design principles ( see GRASP ) and GOF pattern are related to design. Even if GOF patterns, for me, are built with GRASP principles.
Information expert stands that the class that get the information shall get the method providing this information. See information expert
Did you really look on the net before asking your question ?
This is not a code question, but about concepts. I want to know who are the main author/researches for Information Extraction, Natural Language Processing and Text Mining to read his papers/books/works.
You will find very good references on Quora under:
What are the most important research papers which all NLP students should definitely read?
While not a definitive list, the ACL Anthology Network has a list of rankings that give you a sense of what papers are frequently cited in Computational Linguistics.
For me, Daniel Jurafsky, Christopher Manning and Tom Mitchell.
Stanford is offering an online class on natural language processing. Visit http://www.nlp-class.org/
Look at The Handbook of Data Mining - Nong Ye for a collection of many papers. This should also point you to the key researchers in text/data mining.
http://www.amazon.com/Handbook-Mining-Human-Factors-Ergonomics/dp/0805855637/ref=sr_1_1?s=books&ie=UTF8&qid=1328297313&sr=1-1
I for the record own this book.
What would be a good place to go to understand arrows? Ideally, I am just looking for some place with a concise definition with motivation from some good examples, something similar to Wadler's exposition on monads.
http://en.wikibooks.org/wiki/Haskell/Understanding_arrows
I found Hughes' original paper ("Generalizing Monads to Arrows") to be fairly accessible. You can read an older draft of it here. It has some differences from the original paper, which are noted on the bibliography page of Ross Patterson's own overview of Arrows.
If you learn better from practice than theory, try using HXT for XML manipulation, or PArrows for general parsing. They both have APIs centered around arrows.