NLP research notes
What Is Neural Network In Simple Words?
.
A neural network is actually just a mathematical function. You enter a vector of values, those values get multiplied by other values, and a value or vector of values is output.
.
They are very useful in problem domains where there is no known function for approximating the given features (or inputs) to their outputs (classification or regression). One example would be the weather - there are lots of features to the weather - type, temperature, movement, cloud cover, past events, etc - but nobody can say exactly how to calculate what the weather will be 2 days from now. A neural network is a function that is structured in a way that makes it easy to alter its parameters to approximate weather predication based on features.
.
Therefore, Neural Network is a function and has a structure suited to "learning". One would take the past five years of weather data - complete with the features of the weather and the condition of the weather 2 days in the future, for every day in the past five years. The network weights (multiplying factors which reside in the edges) are generated randomly, and the data is run through. For each prediction, the NN will output values that are incorrect.
.
Using a learning algorithm based in calculus, such as back-propagation, one can use the output error values to update all the weights in the network. After enough runs through the data, the error levels will reach some lowest point. The goal is to stop the learning algorithm when error levels are at a best point. The network is then fixed and at this point it is just a mathematical function that maps input values into output values just like any old equation.
.
REFERENCE:
https://softwareengineering.stackexchange.com/questions/72093/what-is-a-neural-network-in-simple-words
.
Read More
Text Processing in Python Text using NLTK and spaCy
.
The Internet has connected the world, while Social Media like Facebook, Twitter and Reddit provided the platform for people to express their opinions and feelings toward a topic. Then, the proliferation of smartphones increased the usage of these platforms directly. For instance, there are 96% or 2,240 million Facebook active users who used Facebook by smartphones and tablets [1].
The increment in the usage of Social Media has grown the size of text data, and boost the studies or researches in Natural Language Processing (NLP), for example, Information Retrieval and Sentiment Analysis. Most of the time, the documents or the text files to be analyzed are gigantic and contains a lot of noise, directly used raw texts for analysis is inapplicable. Hence, text processing is essential to provide clean input for modelling and analysis.
Text processing contains two main phases, which are tokenization and normalization [2]. Tokenization is the process of splitting a longer string of text into smaller pieces, or tokens [3]. Normalization referring to convert number to their word equivalent, remove punctuation, convert all text to the same case, remove stopwords, remove noise, lemmatizing and stemming.
Stemming — removing affixes (suffixed, prefixes, infixes, circumfixes), For example, running to run
Lemmatization — capture canonical form based on a word’s lemma. For example, better to good [4]
.
Text Processing with NLTK
0. Import all needed libraries
1. Tokenization
2. Normalization
a. Removing Stop words
b. Lemmatization
3. Obtain the Cleaned Tokens
Text Processing with spaCy
1. Tokenization + Lemmatization
2. Normalization
a. Removing Noise
b. Removing Stopwords
3. Obtain the Cleaned Tokens
FULL:
https://towardsdatascience.com/text-processing-in-python-29e86ea4114c
References:
[1] M. Iqbal, “Facebook Revenue and Usage Statistics (2020),” 8 March 2021. [Online]. Available: https://www.businessofapps.com/data/facebook-statistics/.
[2] M. Mayo, “A General Approach to Preprocessing Text Data,” 2017. [Online]. Available: https://www.kdnuggets.com/2017/12/general-approach-preprocessing-text-data.html. [Accessed 12 June 2020].
[3] D. Subramanian, “Text Mining in Python: Steps and Examples,” 22 August 2019. [Online]. Available: https://medium.com/towards-artificial-intelligence/text-mining-in-python-steps-and-examples-78b3f8fd913b. [Accessed 12 June 2020].
[4] M. Mayo, “Natural Language Processing Key Terms, Explained,” 2017. [Online]. Available: https://www.kdnuggets.com/2017/02/natural-language-processing-key-terms-explained.html.
[5] “Natural Language Processing In Julia (Text Analysis),” JCharisTech, 1 May 2018. [Online]. Available: https://jcharistech.wordpress.com/2018/05/01/natural-language-processing-in-julia-text-analysis/.
[6] D. Jurafsky and J. H. Martin, “Speech and Language Processing,” 3 December 2020. [Online]. Available: https://web.stanford.edu/~jurafsky/slp3/.
[7] M.F. Goh, “Text Normalization with spaCy and NLTK,” 29 November 2020. [Online]. Available: https://towardsdatascience.com/text-normalization-with-spacy-and-nltk-1302ff430119.
Subscribe to:
Posts (Atom)