Im reusing word2vec for products on my website and users. I would like to say that a user is NEGATIVELY associated to a product if he has visited the page < 5 seconds and POSITIVELY if he spent > 30 seconds on the page. Is there a way to specify this in word2vec? Or is there some other tool that enables this?
Although your question is not well defined but I think you want to store the relation of user with the product which has nothing to do with word2vec. word2vec essentially gives you a mapping from strings to contiguous domain vectors. In your problem you should give a separate new feature of User-Product relationship (NEGATIVE or POSITIVE) along with the word2vec features and you can let the model retrain the word-embeddings according to this new POSITIVE/NEGATIVE feature while solving your particular task. This way the model will adjust the word-embeddings and get some of the desired effect of the POSITIVE/NEGATIVE features.
Please be more elaborate so that I can answer your question in a better way.
Related
So I am doing a project on document similarity and right now my features are only the embeddings from Doc2Vec. Since that is not showing any good results, after hyperparameter optimization and word embedding before the doc embedding... What other features can I add, so as to get better results?
My dataset is 150 documents, 500-700 words each, with 10 topics(labels), each document having one topic. Documents are labeled on a document level, and that labeling is currently used only for evaluation purposes.
Edit: The following is answer to gojomo's questions and elaborating on my comment on his answer:
The evaluation of the model is done on the training set. I am comparing if the label is the same as the most similar document from the model. For this I am first getting the document vector using the model's method 'infer_vector' and then 'most_similar' to get the most similar document. The current results I am getting are 40-50% of accuracy. A satisfactory score would be of at least 65% and upwards.
Due to the purpose of this research and it's further use case, I am unable to get a larger dataset, that is why I was recommended by a professor, as this is a university project, to add some additional features to the document embeddings of Doc2Vec. As I had no idea what he ment, I am asking the community of stackoverflow.
The end goal of the model is to do clusterization of the documents, again the labels for now being used only for evaluation purposes.
If I don't get good results with this model, I will try out the simpler ones mentioned by #Adnan S #gojomo such as TF-IDF, Word Mover's Distance, Bag of words, just presumed I would get better results using Doc2Vec.
You should try creating TD-IDF with 2 and 3 grams to generate a vector representation for each document. You will have to train the vocabulary on all the 150 documents. Once you have the TF-IDF vector for each document, you can use cosine similarity between any two of them.
Here is a blog article with more details and doc page for sklearn.
How are you evaluating the results as not good, and how will you know when your results are adequate/good?
Note that just 150 docs of 400-700 words each is a tiny, tiny dataset: typical datasets used published Doc2Vec results include tens-of-thousands to millions of documents, of hundreds to thousands of words each.
It will be hard for any of the Word2Vec/Doc2Vec/etc-style algorithms to do much with so little data. (The gensim Doc2Vec implementation includes a similar toy dataset, of 300 docs of 200-300 words each, as part of its unit-testing framework, and to eke out even vaguely useful results, it must up the number of training epochs, and shrink the vector size, significantly.)
So if intending to use Doc2Vec-like algorithms, your top priority should be finding more training data. Even if, in the end, only ~150 docs are significant, collecting more documents that use similar domain language can help improve the model.
It's unclear what you mean when you say there are 10 topics, and 1 topic per document. Are those human-assigned categories, and are those included as part of the training texts or tags passed to the Doc2Vec algorithm? (It might be reasonable to include it, depending on what your end-goals and document-similarity evaluations consist of.)
Are these topics the same as the labelling you also mention, and are you ultimately trying to predict the topics, or just using the topics as a check of the similarity-results?
As #adnan-s suggests in the other answer, it may also be worth trying more-simple count-based 'bag of words' document representations, including potentially on word n-grams or even character n-grams, or TF-IDF weighted.
If you have adequate word-vectors, as trained from your data or from other compatible sources, the "Word Mover's Distance" measure can be another interesting way to compute pairwise similarities. (However, it may be too expensive to calculate between many-hundred-word texts - working much faster on shorter texts.)
As others have already suggested your training set of 150 documents probably isn't big enough to create good representations. You could, however, try to use a pre-trained model and infer the vectors of your documents.
Here is a link where you can download a (1.4GB) DBOW model trained on English Wikipedia pages, working with 300-dimensional document vectors. I obtained the link from jhlau/doc2vec GitHub repository. After downloading the model you can use it as follows:
from gensim.models import Doc2Vec
# load the downloaded model
model_path = "enwiki_dbow/doc2vec.bin"
model = Doc2Vec.load(model_path)
# infer vector for your document
doc_vector = model.infer_vector(doc_words)
Where doc_words is a list of words in your document.
This, however, may not work for you in case your documents are very specific. But you can still give it a try.
I have various restaurant labels with me and i have some words that are unrelated to restaurants as well. like below:
vegan
vegetarian
pizza
burger
transportation
coffee
Bookstores
Oil and Lube
I have such mix of around 500 labels. I want to know is there a way pick the similar labels that are related to food choices and leave out words like oil and lube, transportation.
I tried using word2vec but, some of them have more than one word and could not figure out a right way.
Brute-force approach is to tag them manually. But, i want to know is there a way using NLP or Word2Vec to cluster all related labels together.
Word2Vec could help with this, but key factors to consider are:
How are your word-vectors trained? Using off-the-shelf vectors (like say the popular GoogleNews vectors trained on a large corpus of news stories) are unlikely to closely match the senses of these words in your domain, or include multi-word tokens like 'oil_and_lube'. But, if you have a good training corpus from your own domain, with multi-word tokens from a controlled vocabulary (like oil_and_lube) that are used in context, you might get quite good vectors for exactly the tokens you need.
The similarity of word-vectors isn't strictly 'synonymity' but often other forms of close-relation including oppositeness and other ways words can be interchangeable or be used in similar contexts. So whether or not the word-vector similarity-values provide a good threshold cutoff for your particular desired "related to food" test is something you'd have to try out & tinker around. (For example: whether words that are drop-in replacements for each other are closest to each other, or words that are common-in-the-same-topics are closest to each other, can be influenced by whether the window parameter is smaller or larger. So you could find tuning Word2Vec training parameters improve the resulting vectors for your specific needs.)
Making more recommendations for how to proceed would require more details on the training data you have available – where do these labels come from? what's the format they're in? how much do you have? – and your ultimate goals – why is it important to distinguish between restaurant- and non-restaurant- labels?
OK, thank you for the details.
In order to train on word2vec you should take into account the following facts :
You need a huge and variate text dataset. Review your training set and make sure it contains the useful data you need in order to obtain what you want.
Set one sentence/phrase per line.
For preprocessing, you need to delete punctuation and set all strings to lower case.
Do NOT lemmatize or stemmatize, because the text will be less complex!
Try different settings:
5.1 Algorithm: I used word2vec and I can say BagOfWords (BOW) provided better results, on different training sets, than SkipGram.
5.2 Number of layers: 200 layers provide good result
5.3 Vector size: Vector length = 300 is OK.
Now run the training algorithm. The, use the obtained model in order to perform different tasks. For example, in your case, for synonymy, you can compare two words (i.e. vectors) with cosine (or similarity). From my experience, cosine provides a satisfactory result: the distance between two words is given by a double between 0 and 1. Synonyms have high cosine values, you must find the limit between words which are synonyms and others that are not.
My machine learning goal is to search for potential risks (will cost more money) and opportunities (will save money) from a Project Requirements document.
My idea is to classify sentences from the data into one of these categories: Risk, Opportunity and Irrelevant (no risk, no opportunity, default categorie).
I will use a multinomial Bayes classifier for this with tf-dif.
Now I need to have data for my training set and test set. The way I will do this is label every sentence from requirement documents with 1 of the 3 categories. Is this a good approach?
Or should I only label sentences which are obviously a risk/opportunity/irrelevant?
Also, is the Irrelevant categorie a good idea?
I believe the three-class approach is a good one. This is similar to sentiment analysis, where you typically have positive, negative and neutral documents (or sentences). The neutral comprises the vast majority of the instances, so your classification problem will be unbalanced. That is not necessarily an issue, but for difficult problems like this one, a naive bayes classifier might simply classify everything in the neutral/irrelevant bucket since the prior for neutral will be quite high.
your sampling (labeling) should be representative of the reality. Don't try to create a dataset of 1000 risk, 1000 opportunity, 1000 irrelevant. Instead, take a sample of say 10000 requirements, and assign the proper label to each, even if it means having much more 'Irrelevant' than 'Risk' for instance.
text classification models require many instances, since the search space is vast. I wonder if you have considered the fact that to get reliable results (say over 90%), you may need to manually label thousands of instances.
and even if you have thousands of training instances, your problem looks particularly difficult, unless there are some obvious keywords to trigger 'risk' or 'opportunity' that I don't understand. Ask yourself: would this be easy for a human to judge? If you asked 3 judges to classify your requirements, would they all come up with the same answer? If not, then it might be 10s of thousands of training documents that you will need, and the classification accuracy may still be disappointing.
I just wanted to understand (from your experience), that if I have to create a sentiment analysis classification model (using NLTK), what would be a good training data size. For instance if my training data is going to contain tweets, and I intend to classify them as positive,negative and neutral, how many tweets each should I ideally have per category to get a reasonable model working?
I understand that there are many parameters like quality of data, but if one has to get started what might be a good number.
That's a really hard question to answer for people who are not familiar with the exact data, its labelling and the application you want to use it for. But as a ballpark estimate, I would say start with 1,000 examples of each and go from there.
I'm doing some semantic-web/nlp research, and I have a set of sparse records, containing a mix of numeric and non-numeric data, representing entities labeled with various features extracted from simple English sentences.
e.g.
uid|features
87w39423|speaker=432, session=43242, sentence=34, obj_called=bob,favorite_color_is=blue
4535k3l535|speaker=512, session=2384, sentence=7, obj_called=tree,isa=plant,located_on=wilson_street
23432424|speaker=997, session=8945305, sentence=32, obj_called=salty,isa=cat,eats=mice
09834502|speaker=876, session=43242, sentence=56, obj_called=the monkey,ate=the banana
928374923|speaker=876, session=43242, sentence=57, obj_called=it,was=delicious
294234234|speaker=876, session=43243, sentence=58, obj_called=the monkey,ate=the banana
sd09f8098|speaker=876, session=43243, sentence=59, obj_called=it,was=hungry
...
A single entity may appear more than once (but with a different UID each time), and may have overlapping features with its other occurrences. A second data set represents which of the above UIDs are definitely the same.
e.g.
uid|sameas
87w39423|234k2j,234l24jlsd,dsdf9887s
4535k3l535|09d8fgdg0d9,l2jk34kl,sd9f08sf
23432424|io43po5,2l3jk42,sdf90s8df
09834502|294234234,sd09f8098
...
What algorithm(s) would I use to incrementally train a classifier that could take a set of features, and instantly recommend the N most similar UIDs and probability of whether or not those UIDs actually represent the same entity? Optionally, I'd also like to get a recommendation of missing features to populate and then re-classify to get a more certain matches.
I researched traditional approximate nearest neighbor algorithms. such as FLANN and ANN, and I don't think these would be appropriate since they're not trainable (in a supervised learning sense) nor are they typically designed for sparse non-numeric input.
As a very naive first-attempt, I was thinking about using a naive bayesian classifier, by converting each SameAs relation into a set of training samples. So, for each entity A with B sameas relations, I would iterate over each and train the classifier like:
classifier = Classifier()
for entity,sameas_entities in sameas_dataset:
entity_features = get_features(entity)
for other_entity in sameas_entities:
other_entity_features = get_features(other_entity)
classifier.train(cls=entity, ['left_'+f for f in entity_features] + ['right_'+f for f in other_entity_features])
classifier.train(cls=other_entity, ['left_'+f for f in other_entity_features] + ['right_'+f for f in entity_features])
And then use it like:
>>> print classifier.findSameAs(dict(speaker=997, session=8945305, sentence=32, obj_called='salty',isa='cat',eats='mice'), n=7)
[(1.0, '23432424'),(0.999, 'io43po5', (1.0, '2l3jk42'), (1.0, 'sdf90s8df'), (0.76, 'jerwljk'), (0.34, 'rlekwj32424'), (0.08, '09843jlk')]
>>> print classifier.findSameAs(dict(isa='cat',eats='mice'), n=7)
[(0.09, '23432424'), (0.06, 'jerwljk'), (0.03, 'rlekwj32424'), (0.001, '09843jlk')]
>>> print classifier.findMissingFeatures(dict(isa='cat',eats='mice'), n=4)
['obj_called','has_fur','has_claws','lives_at_zoo']
How viable is this approach? The initial batch training would be horribly slow, at least O(N^2), but incremental training support would allow updates to happen more quickly.
What are better approaches?
I think this is more of a clustering than a classification problem. Your entities are data points and the sameas data is a mapping of entities to clusters. In this case, clusters are the distinct 'things' your entities refer to.
You might want to take a look at semi-supervised clustering. A brief google search turned up the paper Active Semi-Supervision for Pairwise Constrained Clustering which gives pseudocode for an algorithm that is incremental/active and uses supervision in the sense that it takes training data indicating which entities are or are not in the same cluster. You could derive this easily from your sameas data, assuming that - for example - uids 87w39423 and 4535k3l535 are definitely distinct things.
However, to get this to work you need to come up with a distance metric based on the features in the data. You have a lot of options here, for example you could use a simple Hamming distance on the features, but the choice of metric function here is a little bit arbitrary. I'm not aware of any good ways of choosing the metric, but perhaps you have already looked into this when you were considering nearest neighbour algorithms.
You can come up with confidence scores using the distance metric from the centres of the clusters. If you want an actual probability of membership then you would want to use a probabilistic clustering model, like a Gaussian mixture model. There's quite a lot of software to do Gaussian mixture modelling, I don't know of any that is semi-supervised or incremental.
There may be other suitable approaches if the question you wanted to answer was something like "given an entity, which other entities are likely to refer to the same thing?", but I don't think that is what you are after.
You may want to take a look at this method:
"Large Scale Online Learning of Image Similarity Through Ranking" Gal Chechik, Varun Sharma, Uri Shalit and Samy Bengio, Journal of Machine Learning Research (2010).
[PDF] [Project homepage]
More thoughts:
What do you mean by 'entity'? Is entity the thing that is referred by 'obj_called'? Do you use the content of 'obj_called' to match different entities, e.g. 'John' is similar to 'John Doe'? Do you use proximity between sentences to indicate similar entities? What is the greater goal (task) of the mapping?