I have question regarding the particular Naive Bayse algorithm that is used in document classification. Following is what I understand:
construct some probability of each word in the training set for each known classification
given a document we strip all the words that it contains
multiply together the probabilities of the words being present in a classification
perform (3) for each classification
compare the result of (4) and choose the classification with the highest posterior
What I am confused about is the part when we calculate the probability of each word given training set. For example for a word "banana", it appears in 100 documents in classification A, and there are totally 200 documents in A, and in total 1000 words appears in A. To get the probability of "banana" appearing under classification A do I use 100/200=0.5 or 100/1000=0.1?
I believe your model will more accurately classify if you count the number of documents the word appears in, not the number of times the word appears in total. In other words
Classify "Mentions Fruit":
"I like Bananas."
should be weighed no more or less than
"Bananas! Bananas! Bananas! I like them."
So the answer to your question would be 100/200 = 0.5.
The description of Document Classification on Wikipedia also supports my conclusion
Then the probability that a given document D contains all of the words W, given a class C, is
http://en.wikipedia.org/wiki/Naive_Bayes_classifier
In other words, the document classification algorithm Wikipedia describes tests how many of the list of classifying words a given document contains.
By the way, more advanced classification algorithms will examine sequences of N-words, not just each word individually, where N can be set based on the amount of CPU resources you are willing to dedicate to the calculation.
UPDATE
My direct experience is based on short documents. I would like to highlight research that #BenAllison points out in the comments that suggests my answer is invalid for longer documents. Specifically
One weakness is that by considering only the presence or absence of terms, the BIM ignores information inherent in the frequency of terms. For instance, all things being equal, we would expect that if 1 occurrence of a word is a good clue that a document belongs in a class, then 5 occurrences should be even more predictive.
A related problem concerns document length. As a document gets longer, the number of distinct words used, and thus the number of values of x(j) that equal 1 in the BIM, will in general increase.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.1529
Related
I have been searching and attempting to implement a word embedding model to predict similarity between words. I have a dataset made up 3,550 company names, the idea is that the user can provide a new word (which would not be in the vocabulary) and calculate the similarity between the new name and existing ones.
During preprocessing I got rid of stop words and punctuation (hyphens, dots, commas, etc). In addition, I applied stemming and separated prefixes with the hope to get more precision. Then words such as BIOCHEMICAL ended up as BIO CHEMIC which is the word divided in two (prefix and stem word)
The average company name length is made up 3 words with the following frequency:
The tokens that are the result of preprocessing are sent to word2vec:
#window: Maximum distance between the current and predicted word within a sentence
#min_count: Ignores all words with total frequency lower than this.
#workers: Use these many worker threads to train the model
#sg: The training algorithm, either CBOW(0) or skip gram(1). Default is 0s
word2vec_model = Word2Vec(prepWords,size=300, window=2, min_count=1, workers=7, sg=1)
After the model included all the words in the vocab , the average sentence vector is calculated for each company name:
df['avg_vector']=df2.apply(lambda row : avg_sentence_vector(row, model=word2vec_model, num_features=300, index2word_set=set(word2vec_model.wv.index2word)).tolist())
Then, the vector is saved for further lookups:
##Saving name and vector values in file
df.to_csv('name-submission-vectors.csv',encoding='utf-8', index=False)
If a new company name is not included in the vocab after preprocessing (removing stop words and punctuation), then I proceed to create the model again and calculate the average sentence vector and save it again.
I have found this model is not working as expected. As an example, calculating the most similar words pet is getting the following results:
ms=word2vec_model.most_similar('pet')
('fastfood', 0.20879755914211273)
('hammer', 0.20450574159622192)
('allur', 0.20118337869644165)
('wright', 0.20001833140850067)
('daili', 0.1990675926208496)
('mgt', 0.1908089816570282)
('mcintosh', 0.18571510910987854)
('autopart', 0.1729743778705597)
('metamorphosi', 0.16965581476688385)
('doak', 0.16890916228294373)
In the dataset, I have words such as paws or petcare, but other words are creating relationships with pet word.
This is the distribution of the nearer words for pet:
On the other hand, when I used the GoogleNews-vectors-negative300.bin.gz, I could not add new words to the vocab, but the similarity between pet and words around was as expected:
ms=word2vec_model.most_similar('pet')
('pets', 0.771199643611908)
('Pet', 0.723974347114563)
('dog', 0.7164785265922546)
('puppy', 0.6972636580467224)
('cat', 0.6891531348228455)
('cats', 0.6719794869422913)
('pooch', 0.6579219102859497)
('Pets', 0.636363685131073)
('animal', 0.6338439583778381)
('dogs', 0.6224827170372009)
This is the distribution of the nearest words:
I would like to get your advice about the following:
Is this dataset appropriate to proceed with this model?
Is the length of the dataset enough to allow word2vec "learn" the relationships between the words?
What can I do to improve the model to make word2vec create relationships of the same type as GoogleNews where for instance word pet is correctly set among similar words?
Is it feasible to implement another alternative such as fasttext considering the nature of the current dataset?
Do you know any public dataset that can be used along with the current dataset to create those relationships?
Thanks
3500 texts (company names) of just ~3 words each is only around 10k total training words, with a much smaller vocabulary of unique words.
That's very, very small for word2vec & related algorithms, which rely on lots of data, and sufficiently-varied data, to train-up useful vector arrangements.
You may be able to squeeze some meaningful training from limited data by using far more training epochs than the default epochs=5, and far smaller vectors than the default size=100. With those sorts of adjustments, you may start to see more meaningful most_similar() results.
But, it's unclear that word2vec, and specifically word2vec in your averaging-of-a-name's-words comparisons, is matched to your end goals.
Word2vec needs lots of data, doesn't look at subword units, and can't say anything about word-tokens not seen during training. An average-of-many-word-vectors can often work as an easy baseline for comparing multiword texts, but might also dilute some word's influence compared to other methods.
Things to consider might include:
Word2vec-related algorithms like FastText that also learn vectors for subword units, and can thus bootstrap not-so-bad guess vectors for words not seen in training. (But, these are also data hungry, and to use on a small dataset you'd again want to reduce vector size, increase epochs, and additionally shrink the number of buckets used for subword learning.)
More sophisticated comparisons of multi-word texts, like "Word Mover's Distance". (That can be quite expensive on longer texts, but for names/titles of just a few words may be practical.)
Finding more data that's compatible with your aims for a stronger model. A larger database of company names might help. If you just want your analysis to understand English words/roots, more generic training texts might work too.
For many purposes, a mere lexicographic comparison - edit distances, count of shared character-n-grams – may be helpful too, though it won't detect all synonyms/semantically-similar words.
Word2vec does not generalize to unseen words.
It does not even work well for wards that are seen but rare. It really depends on having many many examples of word usage. Furthermore a you need enough context left and right, but you only use company names - these are too short. That is likely why your embeddings perform so poorly: too little data and too short texts.
Hence, it is the wrong approach for you. Retraining the model with the new company name is not enough - you still only have one data point. You may as well leave out unseen words, word2vec cannot work better than that even if you retrain.
If you only want to compute similarity between words, probably you don't need to insert new words in your vocabulary.
By eye, I think you can also use FastText without the need to stem the words. It also computes vectors for unknown words.
From FastText FAQ:
One of the key features of fastText word representation is its ability
to produce vectors for any words, even made-up ones. Indeed, fastText
word vectors are built from vectors of substrings of characters
contained in it. This allows to build vectors even for misspelled
words or concatenation of words.
FastText seems to be useful for your purpose.
For your task, you can follow FastText supervised tutorial.
If your corpus proves to be too small, you can build your model starting from availaible pretrained vectors (pretrainedVectors parameter).
While I was classifying and clustering the documents written in natural language, I came up with a question ...
As word2vec and glove, and or etc, vectorize the word in distributed spaces, I wonder if there are any method recommended or commonly used for document vectorization USING word vectors.
For example,
Document1: "If you chase two rabbits, you will lose them both."
can be vectorized as,
[0.1425, 0.2718, 0.8187, .... , 0.1011]
I know about the one also known as doc2vec, that this document has n dimensions just like word2vec. But this is 1 x n dimensions and I have been testing around to find out the limits of using doc2vec.
So, I want to know how other people apply the word vectors for applications with steady size.
Just stacking vectors with m words will be formed m x n dimensional vectors. In this case, the vector dimension will not be uniformed since dimension m will depends on the number of words in document.
If: [0.1018, ... , 0.8717]
you: [0.5182, ... , 0.8981]
..: [...]
m th word: [...]
And this form is not favorable form to run some machine learning algorithms such as CNN. What are the suggested methods to produce the document vectors in steady form using word vectors?
It would be great if it is provided with papers as well.
Thanks!
The most simple approach to get a fixed-size vector from a text, when all you have is word-vectors, to average all the word-vectors together. (The vectors could be weighted, but if they haven't been unit-length-normalized, their raw magnitudes from training are somewhat of an indicator of their strength-of-single-meaning – polysemous/ambiguous words tend to have vectors with smaller magnitudes.) It works OK for many purposes.
Word vectors can be specifically trained to be better at composing like this, if the training texts are already associated with known classes. Facebook's FastText in its 'classification' mode does this; the word-vectors are optimized as much or more for predicting output classes of the texts they appear in, as they are for predicting their context-window neighbors (classic word2vec).
The 'Paragraph Vector' technique, often called 'doc2vec', gives every training text a sort-of floating pseudoword, that contributes to every prediction, and thus winds up with a word-vector-like position that may represent that full text, rather than the individual words/contexts.
There are many further variants, including some based on deeper predictive networks (eg 'Skip-thought Vectors'), or slightly different prediction targets (eg neighboring sentences in 'fastSent'), or other genericizations that can even include a mixture of symbolic and numeric inputs/targets during training (an option in Facebook's StarSpace, which explores other entity-vectorization possibilities related to word-vectors and FastText-like classification needs).
If you don't need to collapse a text to fixed-size vectors, but just compare texts, there are also techniques like "Word Mover's Distance" which take the "bag of word-vectors" for one text, and another, and give a similarity score.
I need to classify website text with zero or more categories/labels (5 labels such as finance, tech, etc). My problem is handling text that isn't one of these labels.
I tried ML libraries (maxent, naive bayes), but they match "other" text incorrectly with one of the labels. How do I train a model to handle the "other" text? The "other" label is so broad and it's not possible to pick a representative sample.
Since I have no ML background and don't have much time to build a good training set, I'd prefer a simpler approach like a term frequency count, using a predefined list of terms to match for each label. But with the counts, how do I determine a relevancy score, i.e. if the text is actually that label? I don't have a corpus and can't use tf-idf, etc.
Another idea , is to user neural networks with softmax output function, softmax will give you a probability for every class, when the network is very confident about a class, will give it a high probability, and lower probabilities to the other classes, but if its insecure, the differences between probabilities will be low and none of them will be very high, what if you define a treshold like : if the probability for every class is less than 70% , predict "other"
Whew! Classic ML algorithms don't combine both multi-classification and "in/out" at the same time. Perhaps what you could do would be to train five models, one for each class, with a one-against-the-world training. Then use an uber-model to look for any of those five claiming the input; if none claim it, it's "other".
Another possibility is to reverse the order of evaluation: train one model as a binary classifier on your entire data set. Train a second one as a 5-class SVM (for instance) within those five. The first model finds "other"; everything else gets passed to the second.
What about creating histograms? You could use a bag of words approach using significant indicators of for e.g. Tech and Finance. So, you could try to identify such indicators by analyzing the certain website's tags and articles or just browse the web for such inidicators:
http://finance.yahoo.com/news/most-common-words-tech-finance-205911943.html
Let's say your input vactor X has n dimensions where n represents the number of indicators. For example Xi then holds the count for the occurence of the word "asset" and Xi+k the count of the word "big data" in the current article.
Instead of defining 5 labels, define 6. Your last category would be something like a "catch-all" category. That's actually your zero-match category.
If you must match the zero or more category, train a model which returns probability scores (such as a neural net as Luis Leal suggested) per label/class. You could than rate your output by that score and say that every class with a score higher than some threshold t is a matching category.
Try this NBayes implementation.
For identifying "Other" categories, dont bother much. Just train on your required categories which clearly identifies them, and introduce a threshold in the classifier.
If the values for a label does not cross a threshold, then the classifier adds the "Other" label.
It's all in the training data.
AWS Elasticsearch percolate would be ideal, but we can't use it due to the HTTP overhead of percolating documents individually.
Classify4J appears to be the best solution for our needs because the model looks easy to train and it doesn't require training of non-matches.
http://classifier4j.sourceforge.net/usage.html
Example:
I have m sets of ~1000 text documents, ~10 are predictive of a binary result, roughly 990 aren't.
I want to train a classifier to take a set of documents and predict the binary result.
Assume for discussion that the documents each map the text to 100 features.
How is this modeled in terms of training examples and features? Do I merge all the text together and map it to a fixed set of features? Do I have 100 features per document * ~1000 documents (100,000 features) and one training example per set of documents? Do I classify each document separately and analyze the resulting set of confidences as they relate to the final binary prediction?
The most common way to handle text documents is with a bag of words model. The class proportions are irrelevant. Each word gets mapped to a unique index. Make the value at that index equal to the number of times that token occurs (there are smarter things to do). The number of features/dimension is then the number of unique tokens/words in your corpus. There are manny issues with this, and some of them are discussed here. But it works well enough for many things.
I would want to approach it as a two stage problem.
Stage 1: predict the relevancy of a document from the set of 1000. For best combination with stage 2, use something probabilistic (logistic regression is a good start).
Stage 2: Define features on the output of stage 1 to determine the answer to the ultimate question. These could be things like the counts of words for the n most relevant docs from stage 1, the probability of the most probable document, the 99th percentile of those probabilities, variances in probabilities, etc. Whatever you think will get you the correct answer (experiment!)
The reason for this is as follows: concatenating documents together will drown you in irrelevant information. You'll spend ages trying to figure out which words/features allow actual separation between the classes.
On the other hand, if you concatenate feature vectors together, you'll run into an exchangeability problem. By that I mean, word 1 in document 1 will be in position 1, word 1 in document 2 will be in position 1001, in document 3 it will be in position 2001, etc. and there will be no way to know that the features are all related. Furthermore, an alternate presentation of the order of the documents would lead to the positions in the feature vector changing its order, and your learning algorithm won't be smart to this. Equally valid presentations of the document orders will lead to completely different results in an entirely non-deterministic and unsatisfying way (unless you spend a long time designing a custom classifier that's not afficted with this problem, which might ultimately be necessary but it's not the thing I'd start with).
I am using document-term vectors to represent a collection of document. I use TF*IDF to calculate the term weight for each document vector. Then I could use this matrix to train a model for document classification.
I am looking forward to classify new document in future. But in order to classify it, I need to turn the document into a document-term vector first, and the vector should be composed of TF*IDF values, too.
My question is, how could I calculate the TF*IDF with just a single document?
As far as I understand, TF can be calculated based on a single document itself, but the IDF can only be calculated with a collection of document. In my current experiment, I actually calculate the TF*IDF value for the whole collection of documents. And then I use some documents as training set and the others as test set.
I just suddenly realized that this seems not so applicable to real life.
ADD 1
So there are actually 2 subtly different scenarios for classification:
to classify some documents whose content are known but label are not
known.
to classify some totally unseen document.
For 1, we can combine all the documents, both with and without labels. And get the TF*IDF over all of them. This way, even we only use the documents with labels for training, the training result will still contain the influence of the documents without labels.
But my scenario is 2.
Suppose I have the following information for term T from the summary of the training set corpus:
document count for T in the training set is n
total number of training documents is N
Should I calculate the IDF of t for a unseen document D as below?
IDF(t, D)= log((N+1)/(n+1))
ADD 2
And what if I encounter a term in the new document which didn't show up in the training corpus before?
How should I calculate the weight for it in the doc-term vector?
TF-IDF doesn't make sense for a single document, independent of a corpus. It's fundamentally about emphasizing relatively rare and informative words.
You need to keep corpus summary information in order to compute TF-IDF weights. In particular, you need the document count for each term and the total number of documents.
Whether you want to use summary information from the whole training set and test set for TF-IDF, or for just the training set is a matter of your problem formulation. If it's the case that you only care to apply your classification system to documents whose contents you have, but whose labels you do not have (this is actually pretty common), then using TF-IDF for the entire corpus is okay. If you want to apply your classification system to entirely unseen documents after you train, then you only want to use the TF-IDF summary information from the training set.
TF obviously only depends on the new document.
IDF, you compute only on your training corpus.
You can add a slack term to the IDF computation, or adjust it as you suggested. But for a reasonable training set, the constant +1 term will not have a whole lot of effect. AFAICT, in classic document retrieval (think: search), you don't bother to do this. Often, they query document will not become part of your corpus, so why would it be part of IDF?
For unseen words, TF calculation is not a problem as TF is a document specific metric. While computing IDF, you can use smoothed inverse document frequency technique.
IDF = 1 + log(total documents / document frequency of a term)
Here the lower bound for IDF is 1. So if a word is not seen in the training corpus, its IDF is 1. Since, there is no universally agreed single formula for computing tf-idf or even idf, your formula for tf-idf calculation is also reasonable.
Note that, in many cases, unseen terms are ignored if they don't have much impact in the classification task. Sometimes, people replace unseen tokens with a special symbol like UNKNOWN_TOKEN and do their computation.
Alternative of TF-IDF: Another way of computing weight of each term of a document is using Maximum Likelihood Estimation. While computing MLE, you can smooth using additive smoothing technique which is also known as Laplace smoothing. MLE is used in case you are using Generative models like Naive Bayes algorithm for document classification.