I have used three different ways to calculate the matching between the resume and the job description. Can anyone tell me that what method is the best and why?
I used NLTK for keyword extraction and then RAKE for
keywords/keyphrase scoring, then I applied cosine similarity.
Scikit for keywords extraction, tf-idf and cosine similarity
calculation.
Gensim library with LSA/LSI model to extract keywords and calculate
cosine similarity between documents and query.
Nobody here can give you the answer. The only way to decide which method works better is to have one or more humans independently match lots and lots of resumes and job descriptions, and compare what they do to what your algorithms do. Ideally you'd have a dataset of already matched resumes and job descriptions (companies must do this kind of thing when people apply), because it takes a lot of work to create a sufficiently large dataset.
Next time you take on this kind of project, start by considering how you are going to evaluate the performance of the solution you'll put together.
As already mentioned in answers, try ti use Doc2Vec.
Seems using Doc2Vec from Gensim on both corpora (CVs and job descriptions) separately and then using cosine similarity between the two vectors is the easiest flow to work. It works better than others on documents which are not similar in form and words content but similar in context and sematics, so merely keywords would not help much here.
Then you can try to train CNN on the corpus of pairs of matched CV&JD with labels like yes/no if available and use it to qulaify CVs/resumees against job descriptions.
Basically I'm going to try these aproaches in my pretty much the same task, pls see https://datascience.stackexchange.com/questions/22421/is-there-an-algorithm-or-nn-to-match-two-documents-basically-not-closely-simila
Since its highly likely that job description and resume content can be different, you should think from semantics point of view. One thing possible you can do is use some domain knowledge. But its pretty difficult to gain domain knowledge for a variety of job types. Researchers sometimes use dictionary to augment the similarity matching between documents.
Researchers are using deep neural networks to capture both syntactic and semantic structure of documents. You can use doc2Vec to compare two documents. Gensim can produce doc2Vec representation for you. I believe that will give better results compared to keyword extraction and similarity computation. You can build your own neural network model to train on job descriptions and resumes. I guess neural networks will be effective for your work.
Related
Does anyone know how of an accurate tool or method that can be used to compute word embeddings or find similarity among domain-specific words? I'm working on an NLP project that involves computing cosine similarity between technical terms, such as "address" and "socket", but pre-trained models like word2vec aren't giving useful embeddings or accurate cosine similarities because they aren't specific to technical terms. Since the more general-nontechnical meanings of "address" and "socket" aren't similar to one another, these pretrained models aren't giving them sufficiently high similarity scores for the purposes of my project. Would appreciate any advice people would be able to offer. Thank you!
With sufficient data from your specific domain, you can train your own word2vec model - whose resulting word-vectors, being only influenced by your domain data, will be far more reflective of the in-domain meanings.
Similarly, if you have a mixture of data where you have hints that some word uses are for different senses of a polysemous word, you could try preprocessing your text, using those hints, replacing the ambiguous tokens (like say 'address') with a larger number of distinct tokens (like 'address*networking', 'address*delivery', etc). Even with a lot of error in such a process, its results might be sufficient for a specific purpose.
For example, maybe you'd assume all docs of a certain type – like articles from a particular publication – always mean 'address*networking' when they write 'address'. That crude replacement, on just some subset of docs sufficient to collect enough varied examples of 'address*networking' usage, might leave you with a good-enough word-vector for 'address*networking'.
(More generally, deciding which word sense of multiple candidates is meant by a particular word is called "word sense disambiguation", and it might be possible to use other preexisting code for performing that to help preprocess texts - replacing ambiguous tokens with more-speciific stand-ins – before performing word2vec training.)
Even without such assistive pre-processing, there've been a number of research attempts to extend word2vec to better model words with multiple contrasting meanings. Googling for [word2vec polysemy] or [polysemous embeddings] should turn up a bunch of examples.
But I don't know any of those techniques that have become widely-used, or that are explicitly supported by major word2vec libraries, so I can't specifically recommend or show working code for any. I don't know a standard best-practice or off-the-shelf solution – you'd have to treat adopting those ideas from research papers as an R&D project, performing a lot of your own implementation/evaluation to see if any help with your goals.
I am working on a text classification problem. The problem is explained below:
I have a dataset of events which contains three columns - name of the event, description of the event, category of the event. There are about 32 categories in the dataset, such as, travel, sport, education, business etc. I have to classify each event to a category depending on its name and description.
What I understood is this particular task of classification is highly dependent on keywords, rather than, semantics. I am giving you two examples:
If the word 'football' is found either in the name or description or in both, it is highly likely that the event is about sport.
If the word 'trekking' is found either in the name or description or in both, it is highly likely that the event is about travel.
We are not considering multiple categories for an event(however, that's a plan for future !! )
I hope applying tf-idf before Multinomial Naive Bayes would lead to decent result for this problem. My question is:
Should I do stop word removal and stemming before applying tf-idf or should I apply tf-idf just on raw text? Here text means entries in name of event and description columns.
The question is too generic and you are not providing samples of the dataset, code, and not even indicating the language you are using. To this regard, I will presume that you are using English, since the two words that you are providing as an example are "football" and "trekking". The answer will however necessarily be generic.
Should I do stop word removal
Yes. Have a look at this to see the most frequent words in the English language. As you can see they have no semantic meaning, and thus would not contribute to solving the classification task that you have proposed. if stopwords is a list containing stopwords, the parameter stop_words=stopwords passed to the CountVectorizer or TfidfVectorizer constructor will automatically exclude the stopwords when invoking the .fit_transform() method.
Should I do stemming
It depends. Languages other than English, whose grammar rules allow for a big number of possible prefixes-suffixes, normally require stemming when performing classification task, in order to reach any useful result. The English language however has very poor grammar rules, and thus you can often get away without stemming/lemmatization. You should check the results obtained against the desired accuracy first, and if it is insufficient, try adding a stemming/lemmatization step in the preprocessing of your data. Stemming is a computationally expensive process for large corpora, and I personally use it only for languages that require it.
I hope applying tf-idf before Multinomial Naive Bayes would lead to decent result for this problem
Careful with this. While tf-idf in practice works with Naive Bayesian classifiers, this is not the way that specific classifier is meant to be used. From the documentation,
The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work. It is in your best interest to tackle the classification task with CountVectorizer first and score it, and after you have a baseline accuracy for evaluating the TfidfVectorizer, check whether its results are better or worse than those of the CountVectorizer.
If you post some code and a sample of your dataset we can help you with that, otherwise this should be enough.
I am clustering a set of education documents using doc2vec.
As a human, I think of these as in categories such as:
computer-related
language related
collaboration
arts
etc.
I wonder if there is a way to 'guide' the doc2vec clustering into a set of clusters that are human-interpretable.
One strategy I have been trying is to filter out all 'nonsense' words, and only train doc2vec on the words that seem meaningful. But of course, this seems to perhaps ruin the training.
Something just occurred to me that might work:
Train on entire documents (don't filter out words) to create doc2vec space
Filter nonsense words ('help', 'student', etc. are words that have very little meaning in this space) out of each document
Project filtered documents into doc2vec space
then process using k-means etc
I would appreciate any constructive suggestions or next steps.
best
Your plan is fine; you should try it to evaluate the results. The clusters may not map tightly to your preconceived groupings, but by looking at the example docs per cluster, you'll probably be able to form your own rough idea of what the cluster "is" in human-crafted descriptive terms.
Don't try too much guesswork preprocessing (like eliminating words) at first. Try those kinds of variations after you have the simplest possible approach working, as a baseline – so you can evaluate (even if only by ad hoc eyeballing) whether they're helping as expected. (For example, if a word like 'student' truly appears across all documents equally, it won't have much influence either way on Doc2Vec final doc coordinates... so you don't have to make that judgement call yourself, it'll just be deemphasized automatically.)
I'm assuming that by Doc2Vec you mean the 'Paragraph Vector' algorithm, as implemented by the Doc2Vec class in Python gensim. Some PV-Doc2Vec modes, including the default PV-DM (dm=1) and also the simpler PV-DBOW if you also enable concurrent word-training (dm=0, dbow_words=1), train word-vectors into the same space as doc-vectors. So the word-vectors that are closest to the doc-vectors in a cluster, or the cluster's centroid, might be useful as interpretable descriptions of the cluster.
(In the word-vector space, there's also research that tries to make the individual dimensions of word-vectors more-interpretable by constraining training in some way, such as requiring vectors to be spares with only non-negative dimensions. See for example this NNSE work and other papers like it. Presumably that might also be applicable to doc-vectors, but I don't know offhand any papers or libraries to do that.)
You could also apply other topic-modeling algorithms, like LDA, that calculate discrete 'topics' that are usually fairly interpretable, and report the strongest topics in each document. (You can cluster on the full doc-topics weights, or perhaps just naively assign each document to its one strongest topic as a simple kind of clustering.)
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.
Background
For years I've been using my own Bayesian-like methods to categorize new items from external sources based on a large and continually updated training dataset.
There are three types of categorization done for each item:
30 categories, where each item must belong to one category, and at most two categories.
10 other categories, where each item is only associated with a category if there is a strong match, and each item can belong to as many categories as match.
4 other categories, where each item must belong to only one category, and if there isn't a strong match the item is assigned to a default category.
Each item consists of English text of around 2,000 characters. In my training dataset there are about 265,000 items, which contain a rough estimate of 10,000,000 features (unique three word phrases).
My homebrew methods have been fairly successful, but definitely have room for improvement. I've read the NLTK book's chapter "Learning to Classify Text", which was great and gave me a good overview of NLP classification techniques. I'd like to be able to experiment with different methods and parameters until I get the best classification results possible for my data.
The Question
What off-the-shelf NLP tools are available that can efficiently classify such a large dataset?
Those I've tried so far:
NLTK
TIMBL
I tried to train them with a dataset that consisted of less than 1% of the available training data: 1,700 items, 375,000 features. For NLTK I used a sparse binary format, and a similarly compact format for TIMBL.
Both seemed to rely on doing everything in memory, and quickly consumed all system memory. I can get them to work with tiny datasets, but nothing large. I suspect that if I tried incrementally adding the training data the same problem would occur either then or when doing the actual classification.
I've looked at Google's Prediction API, which seem to do much of what I'm looking for but not everything. I'd also like to avoid relying on an external service if possible.
About the choice of features: in testing with my homebrew methods over the years, three word phrases produced by far the best results. Although I could reduce the number of features by using words or two word phrases, that would most likely produce inferior results and would still be a large number of features.
After this post and based on the personal experience, I would recommend Vowpal Wabbit. It is said to have one of the fastest text classification algorithms.
MALLET has a number of classifiers (NB, MaxEnt, CRF, etc). It's written Andrew McCallum's group. SVMLib is another good option, but SVM models typically require a bit more tuning than MaxEnt. Alternatively some sort of online clustering like K-means might not be bad in this case.
SVMLib and MALLET are quite fast (C and Java) once you have your model trained. Model training can take a while though! Unfortunately it's not always easy to find example code. I have some examples of how to use MALLET programmatically (along with the Stanford Parser, which is slow and probably overkill for your purposes). NLTK is a great learning tool and is simple enough that is you can prototype what you are doing there, that's ideal.
NLP is more about features and data quality than which machine learning method you use. 3-grams might be good, but how about character n-grams across those? Ie, all the character ngrams in a 3-gram to account for spelling variations/stemming/etc? Named entities might also be useful, or some sort of lexicon.
I would recommend Mahout as it is intended for handling very large scale data sets.
The ML algorithms are built over Apache Hadoop(map/reduce), so scaling is inherent.
Take a look at classification section below and see if it helps.
https://cwiki.apache.org/confluence/display/MAHOUT/Algorithms
Have you tried MALLET?
I can't be sure that it will handle your particular dataset but I've found it to be quite robust in previous tests of mine.
However, I my focus was on topic modeling rather than classification per se.
Also, beware that with many NLP solutions you needn't input the "features" yourself (as the N-grams, i.e. the three-words-phrases and two-word-phrases mentioned in the question) but instead rely on the various NLP functions to produce their own statistical model.