Autoencoders can be used to reduce dimensionallity in feature vectors - as far as I understand. In text classification a feature vector is normally constructed via a dictionary - which tends to be extremely large. I have no experience in using autoencoders, so my questions are:
Could autoencoders be used to reduce dimensionallity in text classification? (Why? / Why not?)
Has anyone already done this? A source would be nice, if so.
The existing works use auto encoder for creating models in the sentence level. Basically after training the model using Autoencode, you can get a vector for a sentence. Since any document consists of sentences you can get a set of vectors for the document, and do the document classification. In my experience with various vector representation (e.g. those generated from autoencodes) doing so might give answers worse than classification with bag of words.
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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.
Good day, fellow humans (?).
I have a methodological question that is confused by a deep research in a tiny amount of time.
The question arises from the following problem(s): I need to apply semi-supervised or unsupervised clustering on documents. I have ~300 documents classified with multi-labels and approximately 3400 documents not classified. The number of unsupervised documents could become ~10'000 in the next days.
The main idea is that of applying semi-supervised clustering based on the labels at hands. Alternatively, that of going fully unsupervised for soft clustering.
We thought of creating embeddings for the whole documents, but here lies the confusion: which library is the best for such a task?
I guess the utmost importance needs to lie in the context of the whole document. As far as I know, BERT and FastText provide context-dependent word embedding, but not whole document embedding. On the other hand, Gensim's Doc2Vec is context-agnostic, right?
I think I saw a way to train sentence embeddings with BERT, via the HuggingFace API, and was wondering whether it could be useful to consider the whole document as a single sentence.
Do you have any suggestion? I'm probably exposing my utter ignorance and confusion on the matter, but my brain is melted.
Thank you very much for your time.
Viva!
Edit to answer to #gojomo:
My documents are on average ~180 words. The original task was that of multi-label text classification, i.e. each document can have from 1 to N labels, with the number of labels now being N=18. They are highly imbalanced.
Having only 330 labeled documents so far due to several issues, we asked the documents' provider to give also unlabeled data, that should reach the order of the 10k.
I used FastText classification mode, but the result is obviously atrocious. I also run a K-NN with Doc2Vec document embedding, but the result is obviously still atrocious.
I was going to use biomedical BERT-based models (like BioBERT and SciBERT) to produce a NER tagging (trained on domain-specific datasets) on the documents to later apply a classifier.
Now that we have unlabeled documents at disposal, we wanted to adventure into semi-supervised classification or unsupervised clustering, just to explore possibilities. I have to say that this is just a master thesis.
I am in a case that I don't have any pre-trained words embedding for my domain (Vietnamese food reviews). so I got a though of embedding from the general and specific corpus.
And the point here is can I use the dataset of training, test and validating (did preprocess) as a source for creating my own word embeddings. If don't, hope you can give your experience.
Based on my intuition, and some experiments a wide corpus appears to be better, but I'd like to know if there's relevant research or other relevant results.
can I use the dataset of training, test and validating (did
preprocess) as a source for creating my own word embeddings
Sure, embeddings are not your features for your machine learning model. They are the "computational representation" of your data. In short, they are made of words represented in a vector space. With embeddings, your data is less sparse. Using word embeddings could be considered part of the pre-processing step of NLP.
Usually (I mean, using the most used technique, word2vec), the representation of a word in the vector space is defined by its surroundings (the words that it commonly goes along with).
Therefore, to create embeddings, the larger the corpus, the better, since it can better place a word vector in the vector space (and hence compare it to other similar words).
I am trying to build a classifier to detect subjectivity. I have text files tagged with subjective and objective . I am little lost with the concept of features creation from this data. I have found the lexicon of the subjective and objective tag. One thing that I can do is to create a feature of having words present in respective dictionary. Maybe the count of words present in subjective and objective dictionary. After that I intend to use naive bayes or SVM to develop the model
My problem is as follow
Is my approach correct ?
Can I create more features ? If possible suggest some or point me to some paper or link
Can I do some test like chi -sq etc to identify effective words from the dictionary ?
You are basically on the right track. I would try and apply classifier with features you already have and see how well it will work, before doing anything else.
Actually best way to improve your work is to google for subjectivity classification papers and read them (there are a quite a number of them). For example this one lists typical features for this task.
And yes Chi-squared can be used to construct dictionaries for text classification (other commonly used methods are TD*IDF, pointwise mutal information and LDA)
Also, recently new neural network-based methods for text classification such as paragraph vector and dynamic convolutional neural networks with k-max pooling demonstrated state-of-the-art results on sentiment analysis, thus they should probably be good for subjectivity classification as well.
How to create word vector? I used one hot key to create word vector, but it is very huge and not generalized for similar semantic word. So I have heard about word vector using neural network that finds word similarity and word vector. So I wanted to know how to generate this vector (algorithm) or good material to start creating word vector ?.
Word-vectors or so-called distributed representations have a long history by now, starting perhaps from work of S. Bengio (Bengio, Y., Ducharme, R., & Vincent, P. (2001).A neural probabilistic language model. NIPS.) where he obtained word-vectors as by-product of training neural-net lanuage model.
A lot of researches demonstrated that these vectors do capture semantic relationship between words (see for example http://research.microsoft.com/pubs/206777/338_Paper.pdf). Also this important paper (http://arxiv.org/abs/1103.0398) by Collobert et al, is a good starting point with understanding word vectors, the way they are obtained and used.
Besides word2vec there is a lot of methods to obtain them. Expamples include SENNA embeddings by Collobert et al (http://ronan.collobert.com/senna/), RNN embeddings by T. Mikolov that can be computed using RNNToolkit (http://www.fit.vutbr.cz/~imikolov/rnnlm/) and much more. For English, ready-made embeddings can be downloaded from these web-sites. word2vec really uses skip-gram model (not neural network model). Another fast code for computing word representations is GloVe (http://www-nlp.stanford.edu/projects/glove/). It is an open question whatever deep neural networks are essential for obtaining good embeddings or not.
Depending of your application, you may prefer using different types of word-vectors, so its a good idea to try several popular algorithms and see what works better for you.
I think the thing you mean is Word2Vec (https://code.google.com/p/word2vec/). It trains N-dimensional word vectors of documents based on a given corpus. So in my understanding of word2vec the neural network is just used to aggregate the dimensions of the document vector and also capturing some relationship between words. But what should be mentioned is that this is not really semantically related, it just reflects the structural relationship in your training body.
If you want to capture semantic relatedness have a look a WordNet based measures, for instance implemented is these libaries:
Java: https://code.google.com/p/ws4j/
Perl: http://wn-similarity.sourceforge.net/
To get started with word2vec you can use their pretrained vectors. You should find all information about this at https://code.google.com/p/word2vec/.
When you seek for a java implementation. This is a good starting point: http://deeplearning4j.org/word2vec.html
I hope this helps
Best wishes