I have a data that represents comments from the operator on various activities performed on a industrial device. The comments, could reflect either a routine maintainence/replacement activity or could represent that some damage occured and it had to be repaired to rectify the damage.
I have a set of 200,000 sentences that needs to be classified into two buckets - Repair/Scheduled Maintainence(or undetermined). These have no labels, hence looking for an unsupervised learning based solution.
Some sample data is as shown below:
"Motor coil damaged .Replaced motor"
"Belt cracks seen. Installed new belt"
"Occasional start up issues. Replaced switches"
"Replaced belts"
"Oiling and cleaning done".
"Did a preventive maintainence schedule"
The first three sentences have to be labeled as Repair while the second three as Scheduled maintainence.
What would be a good approach to this problem. though I have some exposure to Machine learning I am new to NLP based machine learning.
I see many papers related to this https://pdfs.semanticscholar.org/a408/d3b5b37caefb93629273fa3d0c192668d63c.pdf
https://arxiv.org/abs/1611.07897
but wanted to understand if there is any standard approach to such problems
Seems like you could use some reliable keywords (verbs it seems in this case) to create training samples for an NLP Classifier. Or you could use KMeans or KMedioids clustering and use 2 as K, which would do a pretty good job of separating the set. If you want to get really involved, you could use something like Latent Derichlet Allocation, which is a form of unsupervised topic modeling. However, for a problem like this, on the small amount of data you have, the fancier you get the more frustrated with the results you will become IMO.
Both OpenNLP and StanfordNLP have text classifiers for this, so I recommend the following if you want to go the classification route:
- Use key word searches to produce a few thousand examples of your two categories
- Put those sentences in a file with a label based on the OpenNLP format (label |space| sentence | newline )
- Train a classifier with the OpenNLP DocumentClassifier, and I recommend stemming for one of your feature generators
- after you have the model, use it in Java and classify each sentence.
- Keep track of the scores, and quarantine low scores (you will have ambiguous classes I'm sure)
If you don't want to go that route, I recommend using a text indexing technology de-jeur like SOLR or ElasticSearch or your favorite RDBMS's text indexing to perform a "More like this" type function so you don't have to play the Machine learning continuous model updating game.
Related
I am building a model that will predict the lead time of products flowing through a pipeline.
I have a lot of different features, one is a string containing a few words about the purpose of the product (often abbreviations, name of the application it will be a part of and so forth). I have previously not used this field at all when doing feature engineering.
I was thinking that it would be nice to do some type of clustering on this data, and then use the cluster ID as a feature for my model, perhaps the lead time is correlated with the type of info present in that field.
Here was my line of thinking)
1) Cleaning & tokenizing text.
2) TF-IDF
3) Clustering
But after thinking more about it, is it a bad idea? Because the clustering was based on the old data, if new words are introduced in the new data this will not be captured by the clustering algorithm, and the data should perhaps be clustered differently now. Does this mean that I would have to retrain the entire model (k-means model and then the supervised model) whenever I want to predict new data points? Are there any best practices for this?
Are there better ways of finding clusters for text data to use as features in a supervised model?
I understand the urge to use an unsupervised clustering algorithm first to see for yourself, which clusters were found. And of course you can try if such a way helps your task.
But as you have labeled data, you can pass the product description without an intermediate clustering. Your supervised algorithm shall then learn for itself if and how this feature helps in your task (of course preprocessing such as removal of stopwords, cleaining, tokenizing and feature extraction needs to be done).
Depending of your text descriptions, I could also imagine that some simple sequence embeddings could work as feature-extraction. An embedding is a vector of for example 300 dimensions, which describes the words in a manner that hp office printer and canon ink jet shall be close to each other but nice leatherbag shall be farer away from the other to phrases. For example fasText-Word-Embeddings are already trained in english. To get a single embedding for a sequence of hp office printerone can take the average-vector of the three vectors (there are more ways to get an embedding for a whole sequence, for example doc2vec).
But in the end you need to run tests to choose your features and methods!
We are currently working on integrating ICD10-CM for our medical company, to be used for patient diagnosis. ICD10-CM is a coding system for diagnoses.
I tried to import ICD10-CM data in description-code pairs but obviously, it didn't work since AutoML needed more text for that code(label). I found a dataset on Kaggle but it only contained hrefs to an ICD10 website. I did find out that the website contains multiple texts and descriptions associated with codes that can be used to train our desired model.
Kaggle Dataset:
https://www.kaggle.com/shamssam/icd10datacom
Sample of a page from ICD10data.com:
https://www.icd10data.com/ICD10CM/Codes/A00-B99/A15-A19/A17-/A17.0
Most notable fields are:
- Approximate Synonyms
- Clinical Information
- Diagnosis Index
If I made a dataset from the sentences found in these pages and assigned them to their code(labels), will it be enough for AutoML dataset training? since each label will have 2 or more texts finally instead of just one, but definitely still a lot less than a 100 for each code unlike those in demos/tutorials.
From what I can see here, the disease code has a tree-like structure where, for instance, all L00-L99 codes refer to "Diseases of the skin and subcutaneous tissue". At the same time L00-L08 codes refer to "Infections of the skin and subcutaneous tissue", and so on.
What I mean is that the problem is not 90000 examples for 90000 different independent labels, but a decision tree (you take several decisions in function of the previous decision: the first step would be choosing which of the about 15 most general categories fits best, then choosing which of the subcategories etc.)
In this sense, probably autoML is not the best product, given that you cannot implement a specially designed decision tree model that takes into account all of this.
Another way of using autoML would be training separately for each of the decisions and then combine the different models. This would easily work for the first layer of decision but would be exponentially time consuming (the number of models to train in order to be able to predict more accurately grows exponentially with the level of accuracy, by accurate I mean afirminng it is L00-L08 instad of L00-L99).
I hope this helps you understand better the problem and the different approaches you can give to it!
I'm trying to classify some data using knime with knime-labs deep learning plugin.
I have about 16.000 products in my DB, but I have about 700 of then that I know its category.
I'm trying to classify as much as possible using some DM (data mining) technique. I've downloaded some plugins to knime, now I have some deep learning tools as some text tools.
Here is my workflow, I'll use it to explain what I'm doing:
I'm transforming the product name into vector, than applying into it.
After I train a DL4J learner with DeepMLP. (I'm not really understand it all, it was the one that I thought I got the best results). Than I try to apply the model in the same data set.
I thought I would get the result with the predicted classes. But I'm getting a column with output_activations that looks that gets a pair of doubles. when sorting this column I get some related date close to each other. But I was expecting to get the classes.
Here is a print of the result table, here you can see the output with the input.
In columns selection it's getting just the converted_document and selected des_categoria as Label Column (learning node config). And in Predictor node I checked the "Append SoftMax Predicted Label?"
The nom_produto is the text column that I'm trying to use to predict the des_categoria column that it the product category.
I'm really newbie about DM and DL. If you could get me some help to solve what I'm trying to do would be awesome. Also be free to suggest some learning material about what attempting to achieve
PS: I also tried to apply it into the unclassified data (17,000 products), but I got the same result.
I won't answer with a workflow on this one because it is not going to be a simple one. However, be sure to find the text mining example on the KNIME server, i.e. the one that makes use of the bag of words approach.
The task
Product mapping to categories should be a straight-forward data mining task because the information that explains the target variable is available in a quasi-exhaustive manner. Depending on the number of categories to train though, there is a risk that you might need more than 700 instances to learn from.
Some resources
Here are some resources, only the first one being truly specialised in text mining:
Introduction on Information Retrieval, in particular chapter 13;
Data Science for Business is an excellent introduction to data mining, including text mining (chapter 10), also do not forget the chapter about similarity (chapter 6);
Machine Learning with R has the advantage of being accessible enough (chapter 4 provides an example of text classification with R code).
Preprocessing
First, you will have to preprocess your product labels a bit. Use KNIME's text analytics preprocessing nodes for that purpose, that is after you've transformed the product labels with Strings to Document:
Case Convert, Punctuation Erasure and Snowball Stemmer;
you probably won't need Stop Word Filter, however, there may be quasi-stop words such as "product", which you may need to remove manually with Dictionary Filter;
Be careful not to use any of the following without testing testing their impact first: N Chars Filter (g may be a useful word), Number Filter (numbers may indicate quantities, which may be useful for classification).
Should you encounter any trouble with the relevant nodes (e.g. Punctuation Erasure can be tricky amazingly thanks to the tokenizer), you can always apply String Manipulation with regex before converting the Strings to Document.
Keep it short and simple: the lookup table
You could build a lookup table based on the 700 training instances. The book Data mining techniques as well as resource (2) present this approach in some detail. If any model performs any worse than the lookup table, you should abandon the model.
Nearest neighbors
Neural networks are probably overkill for this task.
Start with a K Nearest Neighbor node (applying a string distance such as Cosine, Levensthein or Jaro-Winkler). This approach requires the least amount of data wrangling. At the very least, it will provide an excellent baseline model, so it is most definitely worth a shot.
You'll need to tune the parameter k and to experiment with the distance types. The Parameter Optimization Loop pair will help you with optimizing k, you can include a Cross-Validation meta node inside of the said loop to obtain an estimate of the expected performance given k instead of only one point estimate per value of k. Use Cohen's Kappa as an optimization criterion, as proposed by the resource number (3) and available via the Scorer node.
After the parameter tuning, you'll have to evaluate the relevance of your model using yet another Cross-Validation meta node, then follow up with a Loop pair including Scorer to calculate the descriptives on performance metric(s) per iteration, finally use Statistics. Kappa is a convenient metric for this task because the target variable consists of many product categories.
Don't forget to test its performance against the lookup table.
What next ?
Should lookup table or k-nn work well for you, then there's nothing else to add.
Should any of those approaches fail, you might want to analyse the precise cases on which it fails. In addition, training set size may be too low, so you could manually classify another few hundred or thousand instances.
If after increasing the training set size, you are still dealing with a bad model, you can try the bag of words approach together with a Naive Bayes classifier (see chapter 13 of the Information Retrieval reference). There is no room here to elaborate on the bag of words approach and Naive Bayes but you'll find the resources here above useful for that purpose.
One last note. Personally, I find KNIME's Naive Bayes node to perform poorly, probably because it does not implement Laplace smoothening. However, KNIME's R Learner and R Predictor nodes will allow you to use R's e1071 package, as demonstrated by resource (3).
In normal case I had tried out naive bayes and linear SVM earlier to classify data related to certain specific type of comments related to some page where I had access to training data manually labelled and classified as spam or ham.
Now I am being told to check if there are any ways to classify comments as spam where we don't have a training data. Something like getting two clusters for data which will be marked as spam or ham given any data.
I need to know certain ways to approach this problem and what would be a good way to implement this.
I am still learning and experimenting . Any help will be appreciated
Are the new comments very different from the old comments in terms of vocabulary? Because words is almost everything the classifiers for this task look at.
You always can try using your old training data and apply the classifier to the new domain. You would have to label a few examples from your new domain in order to measure performance (or better, let others do the labeling in order to get more reliable results).
If this doesn't work well, you could try domain adaptation or look for some datasets more similar to your new domain, using Google or looking at this spam/ham corpora.
Finally, there may be some regularity or pattern in your new setting, e.g. downvotes for a comment, which may indicate spam/ham. In such cases, you could compile training data yourself. This would them be called distant supervision (you can search for papers using this keyword).
The best I could get to was this research work which mentions about active learning. So what I came up with is that I first performed Kmeans clustering and got the central clusters (assuming 5 clusters I took 3 clusters descending ordered by length) and took 1000 msgs from each. Then I would assign it to be labelled by the user. The next process would be training using logistic regression on the labelled data and getting the probabilities of unlabelled data and then if I have probability close to 0.5 or in range of 0.4 to 0.6 which means it is uncertain I would assign it to be labelled and then the process would continue.
I would like to use a supervised machine learning algorithm to predict a binary function (true or false) for a set of sentences based on the presence or absence of words in the sentences.
Ideally, I would like to avoid having to hardcode the set of words used to decide on the output so that the algorithm automatically learns which words are (together ?) most likely to trigger specific outputs.
http://shop.oreilly.com/product/9780596529321.do (Programming Collective Intelligence) has a nice section in chapter 4 titled "Learning From Clicks" which describes how to do this by using 1 layer of hiden nodes in a neural network with one new hidden node for each new combination of input words.
Similarly, it is possible to create a feature for each word in the training data set and train pretty much any classic machine learning algorithm using these features. Adding new training data will generate new features which will require me to re-train the algorithm from scratch.
Which brings me to my questions:
is it actually a problem if I have to retrain everything from scratch whenever the training data set is extended ?
what kind of algorithm would more experience machine learning users recommend to use for this kind of problem ?
what criteria should I use in picking an algorithm versus another ? (other than actually trying them all and see which perform better with precision/recall metrics)
if you have worked on similar problems, what about extending the features with 2-grams (1 if a specific 2-gram is present, 0 if not) ? 3-grams ?
You could look into the general area of topic modelling if you want to find words which are generally found together.
The most simple approach would be to use latent semantic analysis ( http://en.wikipedia.org/wiki/Latent_semantic_analysis ), which is just applying SVD to a term document matrix. You'd then need to do some additional post hoc analysis to fit this to your particular outcome.
A more involved, and much more complex approach would be to use latent dirichlet allocation ( http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation )
In terms of just adding new features (words) that is fine as long as you are going to retrain. You can also use TF/IDF to give that particular word a value when representing the matrix (Instead of just a 1 or 0).
I don't know what programming language you are trying to do this in, but I know there are libraries out there in Java and Pythont hat do all of the above.