Some applied NLP

From Helpful
(Redirected from Bag-of-words)
Jump to: navigation, search
This is more for overview of my own than for teaching or exercise.

Overview of the areas

Arithmetic · 'elementary mathematics' and similar concepts
Set theory, Category theory
Geometry and its relatives · Topology
Elementary algebra - Linear algebra - Abstract algebra
Calculus and analysis
Logic
Semi-sorted
 : Information theory · Number theory · Decision theory, game theory · Recreational mathematics · Dynamical systems · Unsorted or hard to sort


Math on data:

  • Statistics as a field
some introduction · areas of statistics
types of data · on random variables, distributions
Virtues and shortcomings of...
on sampling · probability
glossary · references, unsorted
Footnotes on various analyses

Other data analysis, data summarization, learning

Regression · Classification, clustering, decisions · dimensionality reduction · Optimization theory, control theory
Connectionism, neural nets · Evolutionary computing



Data massage

Bag of words / bag of features

The bag-of-words model (more broadly bag-of-features model) use the collection of words in a context, unordered, in a multiset, a.k.a. bag.

In other words, we summarize a document (or part of it) it by appearance/count of words and ignore order (and thereby things grammar and adjacency).



In text processing

In introductions to Naive Bayes as used for spam filtering, its naivety essentially is this assumption that feature order does not matter.


Though real-world naive bayes spam filtering would take more complex features than single words (and may re-introduce adjacenct via n-grams or such), examples often use 1-grams for simplicity - which basically is bag of words, exc.

Other types of classifiers also make this assumption, or make it easy to do so.


Bag of features

While the idea is best known from text, you can argue for bag of features, applying it to anything you can count and useful even when considered independently.

For example, you may follow up object detection in an image with logic like "if this photo contains a person and dog and a porch, anywhere" because that tends to narrow down what kind of photo it is.


In practice, the bag-of-features often refers to models that recognize parts of a whole object (e.g. "we detected a bunch of edges of road signs" might be easier and more robust than detecting it fully), and used in a number image tasks, such as feature extraction, object/image recognition, image search, (more flexible) near-duplicate detection, and such.

The idea that you can describe an image by the collection of small things we recognize in it, and that combined presence is typically already a strong indicator (particularly when you add some hypothesis testing). Exact placement can be useful, but often easily secondary.

See also:

N-gram notes

N-grams are contiguous sequence of length n.


They are most often seen in computational linguistics.


Applied to sequences of characters it can be useful e.g. in language identification, but the more common application is to words.

As n-grams models only include dependency information when those relations are expressed through direct proximity, they are poor language models, but useful to things working off probabilities of combinations of words, for example for statistical parsing, collocation analysis, text classification, sliding window methods (e.g. sliding window POS tagger), (statistical) machine translation, and more


For example, for the already-tokenized input
This
is
a
sentence
.
the 2-grams would be:
This
,
is
is
,
a
a
,
sentence
sentence
,
.


Skip-grams

An extension of n-grams that allow allowing a certain amount of arbitrarily positioned omissions from the input.

Skip-grams come from speech analysis, processing phonemes.

In word-level analysis their purpose is a little different. You could say that we acknowledge the sparsity problem, and decide to get more out of the data we have (focusing on context) rather than trying to smooth.



Flexgrams

Words as features

Word embeddings

Word2vec

Continuous bag of words (cbow)

Continuous skip-grams

GloVe