All Questions
Tagged with bag-of-words machine-learning
16 questions
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Continuous Bag of Words NY Time Corpus
I am working to implement the continuous bag of words approach on the New York Times corpus dataset. However, I am getting word embeddings that do not seem very useful based on a few examples of ...
- 51
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1
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could someone please give a concrete example to illustrate the Dirichlet distribution prior for bag-of-words?
I am aware of the notion of the Dirichlet distribution, a multivariate generalization of the beta distribution.
To get parameters of the Dirichlet distribution prior for bag-of-words, this CMU ...
- 2,005
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2
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263
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could someone please give an concrete example to illustrate what does Multiplicity mean in the context of Bag-of-words model?
This CMU Machine Learning Course is using the Bag-of-words model without too much explanation.
wiki uses the term multiplicity to explain that model.
The bag-of-words model is a simplifying ...
- 2,005
1
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2
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Text classification with small dataset for a specialized domain
I have a multiclass text classification problem where I have very few documents for each class. The classes are imbalanced but I want to be able to predict the class when I have at least 200 - 300 ...
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71
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Bag of Visual Words: is feature extraction even needed?
I'm currently implementing a BoVW as part of my lab project. The steps the algorithm used are as follows:
spliting all photos into patches
cluster these pathces using K-means based on pixel values of ...
- 107
1
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1
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Group of word representations
For word representation baseline people use bag-of-words or word embedding. Here, I want to understand all approaches that can be used for word representations. For example:
-Bag-of-words (tfidf, n-...
- 11
1
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82
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What is the difference between training examples generated by continuous bag of words (CBOW) and skip-gram?
This is a simple question that is hard for me:
Let's consider simple sentence
A B C D
and create training examples for skip-gram training (x, y) with number of ...
- 498
3
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Regularization in text classification with bag-of-words
I am performing a text categorization with bag of words and logistic regression.
I have already heard about L1 and L2 regularization and used them for classification but with problems handling way ...
- 203
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412
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Low score in sentiment analysis : how to increase it and maybe deal with class imbalance
It has been 2 weeks now I am working on SemEval task 4 (2016) : Sentiment Analysis on Twitter.
The results I achieve are lower than what I expected for the three class classification problem : ...
- 203
1
vote
1
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90
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Is machine learning a viable approach to extract license references from source code files?
I am a complete newcomer to the field of machine learning. I do have a lot of experience in computer programming, but nothing related to ML.
My question is whether or not ML would be a good approach ...
- 11
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Searching for list of terms using Google in order to build a bag-of-words for a particular category [closed]
I am having a hard time understanding the process of building a bag-of-words. This will be a multiclass classification supervised machine learning problem wherein a webpage or a piece of text is ...
user162992
3
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2
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453
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Language Modelling using Neural Networks
I plan to make a Language Model in Python using Neural Networks. I've read that Neural Networks need vectors as input. One common vector representation in NLP is the Bag of Words model. Given a corpus ...
- 53
4
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321
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Google gender-pay gap vs
Background:
I read this:
google schools US government about gender pay gap.
It derives from this google blog post by Eileen Naughton, VP of People Operations.
She asserts that google is somehow "...
- 9,853
1
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1
answer
2k
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How does a bag-of-words model treat words that were never seen before (not in the training data)?
What happens when a text classifier using a bag-of-words model (let's say we're using logistic regression) encounters a word that the model has not seen before- aka, words that were not in the ...
- 113
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197
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Calculate predictability of events over time
I'm trying to create a model / algorithm which learns the predictability of events over time, which takes into account both frequency and rarity.
An example of what this could apply to (which is the ...
- 101
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61
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Treating numerals/cardinals in Bag of Words (BOW) model
I wish to do topic modeling on text corpus some of which are about company earnings which has lots of numbers in it. It has no sentence structure. I think tagging numbers using nltk.pos_tagging can ...
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