Revision as of 13:46, 28 March 2024

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

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, hence bag-of-words, you can argue for bag of features, applying it to anything you can count, and may be 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 a dog, and grass" because each task may be easy enough individually, and the combination 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.

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 .

...though depending on how special you do or do not want to treat the edges, people might fake some empty tokens at the edge, or some special start/end tokens.

Skip-grams

✎ This article/section is a stub — some half-sorted notes, not necessarily checked, not necessarily correct. Feel free to ignore, or tell me about it.

Note: Skip-grams seem to refer to now two different things.

An extension of n-grams where components need not be consecutive (though typically stay ordered).

A k-skip-n-gram is a length-n sequence where the components occur at distance at most k from each other.

They may be used to ignore stopwords, but perhaps more often they are intended to help reduce data sparsity, under a few assumptions.

They can help discover patterns on a larger scale, simply because skipping makes you look further for the same n. (also useful for things like fuzzy hashing).

Skip-grams apparently 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.

Actually, if you go looking, skip-grams are now often equated with a fairly specific analysis.

Syntactic n-grams

Flexgrams

Words as features - one-hot coding and such

Putting numbers to words

Computers and people and numbers

vector space representations, word embeddings, and more

Contextual word embeddings

Subword embeddings

Bloom embeddings, a.k.a. the hash trick

Moderately specific ideas and calculations

Collocations

Collocations are statistically idiosyncratic sequences - the math that is often used asks "do these adjacent words occur together more often than the occurrence of each individually would suggest?".

This doesn't ascribe any meaning, it just tends to signal anything from empty habitual etiquette, jargon, various substituted phrases, and many other things that go beyond purely compositional construction, because why other than common sentence structures would they co-occur so often?

...actually, there are varied takes on how useful collocations are, and why.

latent semantic analysis

Latent Semantic Analysis (LSA) is the application of Singular Value Decomposition on text analysis and search.

random indexing

https://en.wikipedia.org/wiki/Random_indexing

Topic modeling

Roughly the idea given documents that are about a particular topic, one would expect particular words to appear in the each more or less frequently.

Assuming such documents sharing topics, you can probably find groups of words that belong to those topics.

Assuming each document is primarily about one topic, you can expect a larger set of documents to yield multiple topics, and an assignment of one or more of these topics, so act like a soft/fuzzy clustering.

This is a relatively weak proposal in that it relies on a number of assumptions, but given that it requires zero training, it works better than you might expect when those assumptions are met. (the largest probably being your documents having singular topics).

https://en.wikipedia.org/wiki/Topic_model

word2vec

✎ This article/section is a stub — some half-sorted notes, not necessarily checked, not necessarily correct. Feel free to ignore, or tell me about it.

word2vec is one of many ways to put semantic vectors to words (in the distributional hypothesis approach), and refers to two techniques, using either bag-of-words and skip-gram as processing for a specific learner, as described in T Mikolov et al. (2013), "Efficient Estimation of Word Representations in Vector Space", probably the one that kicked off this dense-vector idea into the interest.

Word2vec amounts could be seen as building a classifier that predicts what word apear in a context, and/or what context appears around a word, which happens to do a decent task of classifying that word.

That paper mentions

its continuous bag of words (cbow) variant predicts the current word based on the words directly around it (ignoring order, hence bow(verify))

its continuous skip-gram variant predicts surrounding words given the current word.

Uses skip-grams as a concept/building block. Some people refer to this technique as just 'skip-gram' without the 'continuous',

but this may come from not really reading the paper you're copy-pasting the image from?

seems to be better at less-common words, but slower

(NN implies one-hot coding, so not small, but it turns out to be moderately efficient(verify))

GloVe

@@ Line 365: / Line 365: @@
-There is a whole trend in, instead of using well-annotated data (which is costly to come by),
-yuou can use orders more ''non-annotated'' data (much easier to come by)
-based on assumptions like the [[distributional hypothesis]] makes:
-that words in similar context, and training from nearby words, is enough to give a good sense of comparability.
-Years ago that was done with things like linear algebra (see e.g. [[Latent Semantic Analysis]]),
-now it is done with more complex math, and/or neural nets,
-which is more polished way of training faster - though the way you handle its output is much the same.
@@ Line 381: / Line 373: @@
-'''Word embeddings''' sometimes have a much more specific meaning, a specific way of finding and using text vectors.
+There are varied ways to do this.
-(There are varied definitions, and you can argue that under some, terms like 'static word embeddings' make no sense)
+One way might be to use well-annotated data, but that is constly to come by.
-but the terms are mixed so much that you should probably read 'embeddings' as 'text vectors'
-and figure out yourself what the implementation actually is.
----
+A recent trend is to use non-annotated data, and a lot more of it.
+Another is to trust the assumptions of the [[distributional hypothesis]] makes,
+e.g. that words in similar context will be comparable,
+and focus on words in context.
+This was done in varied ways over the years (e.g. [[Latent Semantic Analysis]] applies somewhat),
+later with more complex math, and/or neural nets.
+Which may just train better - the way you handle its output is much the same.
+One of the techniques that kicked this off in more recent years is [[word2vec]],
+which doesn't do a lot more than looking what appears in similar contexts.
+(Its view is surprisingly narrow - what happens in a small window in a ''lot'' of data will tend to be more consistent.
+A larger window is not only more work but often too fuzzy)
+Its math is apparently {{search|Levy "Neural Word Embedding as Implicit Matrix Factorization"|fairly like the classical matrix factorization}}.
+'''Word embeddings''' often refers to learning vectors from context,
+though there are more varied meanings (some conflicting),
+so you may wish to read 'embeddings' as 'text vectors' and figure out yourself what the implementation actually is.
+---
 In the context of some of the later developments, the simpler variant implmentations are considered static vectors.
@@ Line 398: / Line 410: @@
 * learn vector for each item in the vocabulary
-Static vectors /
+---
+Using ebeddings
+* use the vectors as-is
+* adapt the embeddings with your own training
+:: starts with a good basis, refines for your use
+:: but: only deals with tokens already in there
+* there are also some ways to selectively alter vectors
+:: can be useful if you want to keep sharing the underling embeddings

Data modeling, restructuring, and massaging: Difference between revisions