Assume you have a (wikipedia) pre-trained word2vec model, and train it on an additional corpus (very small, 1000 scentences).
Can you imagine a way to limit a vector-search to the "re-trained" corpus only?
For example
model.wv.similar_by_vector()
will simply find the closest word for a given vector, no matter if it is part of the Wikipedia corpus, or the re-trained vocabulary.
On the other hand, for 'word' search the concept exists:
most_similar_to_given('house',['garden','boat'])
I have tried to train based on the small corpus from scratch, and it somewhat works as expected. But of course could be much more powerful if the assigned vectors come from a pre-trained set.
Sharing an efficient way to do this manually:
re-train word2vec on the additional corpus
create full unique word-index of corpus
fetch re-trained vectors for each word in the index
instead of the canned function "similar_by_vector", use scipy.spatial.KDTree.query()
This finds the closest word within the given corpus only and works as expected.
Similar to the approach for creating a subset of doc-vectors in a new KeyedVectors instance suggested here, assuming small_vocab is a list of the words in your new corpus, you could try:
subset_vectors = WordEmbeddingsKeyedVectors(vector_size)
subset_vectors.add(small_vocab, w2v_model.wv[small_vocab])
Then subset_vectors contains just the words you've selected, but supports familiar operations like most_similar().
Related
I have a dataset of 33 words that are a mix of verbs and nouns, for eg. father, sing, etc. I have tried converting them to 1-hot encoding but for my use case, it has been suggested to look into word2vec embedding. I have looked in gensim and glove but struggling to make it work.
How could I convert my data into an embedding? Such that two words that may be semantically closer may have a lesser distance between their respective vectors. How may this be achieved or any helpful material on the same?
Such as this
Since your dataset is quite small, and I'm assuming it doesn't contain any jargon, it's best to use a pre-trained model in order to save up on training time.
With gensim, it's as simple as:
import gensim.downloader as api
wv = api.load('word2vec-google-news-300')
The 'word2vec-google-news-300' model has been pre-trained on a part of the Google News Dataset and generalizes well enough to most tasks. Following this, you can create word embeddings/vectors like so:
vec = wv['father']
And, finally, for computing word similarity:
similarity_score = wv.similarity('father', 'sing')
Lastly, one major limitation of Word2Vec is it's inability to deal with words that are OOV(out of vocabulary). For such cases, it's best to train a custom model for your corpus.
I have implemented word2vec on my corpus using the TensorFlow tutorial: https://www.tensorflow.org/tutorials/text/word2vec#next_steps
Now I'm want to give a sentence as input and want to find a similar sentence in the corpus.
Any leads on how I can perform this?
A simple word2vec model is not capable of such task, as it only relates word semantics to each other, not the semantics of whole sentences. Inherently, such a model has no generative function, it only serves as a look-up table.
Word2vec models map word strings to vectors in the embedding space. To find similar words for a given sample word, one can simply go through all vectors in the vocabulary and find the ones that are closest (in terms of the 2-norm) from the sample word vector. For further information you could go here or here.
However, this does not work for sentences as it would require a whole vocabulary of sentences of which to pick similar ones - which is not feasible.
Edit: This seems to be a duplicate of this question.
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 been doing clustering of a certain corpus, and obtaining results that group sentences together by obtaining their tf-idf, checking similarity weights > a certain threshold value from the gensim model.
tfidf_dic = DocSim.get_tf_idf()
ds = DocSim(model,stopwords=stopwords, tfidf_dict=tfidf_dic)
sim_scores = ds.calculate_similarity(source_doc, target_docs)
The problem is that despite putting high threshold values, sentences of similar topics but opposite polarities get clustered together as such:
Here is an example of the similarity weights obtained between "don't like it" & "i like it"
Are there any other methods, libraries or alternative models that can differentiate the polarities effectively by assigning them very low similarities or opposite vectors?
This is so that the outputs "i like it" and "dont like it" are in separate clusters.
PS: Pardon me if there are any conceptual errors as I am rather new to NLP. Thank you in advance!
The problem is in how you represent your documents. Tf-idf is good for representing long documents where keywords play a more important role. Here, it is probably the idf part of tf-idf that disregards the polarity because negative particles like "no" or "not" will appear in most documents and they will always receive a low weight.
I would recommend trying some neural embeddings that might capture the polarity. If you want to keep using Gensim, you can try doc2vec but you would need quite a lot of training data for that. If you don't have much data to estimate the representation, I would use some pre-trained embeddings.
Even averaging word embeddings (you can load FastText embeddings in Gensim). Alternatively, if you want a stronger model, you can try BERT or another large pre-trained model from the Transformers package.
Unfortunately, simple text representations based merely on the sets-of-words don't distinguish such grammar-driven reversals-of-meaning very well.
The method needs to be sensitive to meaningful phrases, and the hierarchical, grammar-driven inter-word dependencies, to model that.
Deeper neural networks using convolutional/recurrent techniques do better, or methods which tree-model sentence-structure.
For ideas see for example...
"Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank"
...or a more recent summary presentation...
"Representations for Language: From Word Embeddings to Sentence Meanings"
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).