Photo by on Michael Unsplash The Right Way to Do NLP by Apache Spark Have you ever wondered if you could say what’s in the book without actually reading it? What if you could draw a map of all the important characters, places, events, and the relations among them? This is where (NLP) and techniques can help us to understand the natural language data in a new and different way. Natural Language Processing Text mining OK! Maybe “reading a book” is not a good example as everyone should read at least two to four books per month! This is a series of articles for exploring “Mueller Report” by using Spark NLP library built on top of Apache Spark and pre-trained models powered by TensorFlow and BERT. These articles are purely educational for those interested in learning how to do NLP by using Apache Spark. Perform NLP tasks and annotating the “ by using pre-trained pipelines and models provided by Spark NLP. First part: Mueller Report” Use models trained by BERT, training a POS tagger model in Spark NLP, data cleaning, and extracting keywords/phrases by POS and NER chunking. Second part : Graph algorithms by , clusterings and topic modelings by Spark ML, and network visualization by . Third part: GraphFrames Gephi (Extracting keywords from the Mueller Report by using Spark NLP) Report on the Investigation into Russian Interference in the 2016 Presidential Election Commonly known as “ ” Muller report (Robert Mueller, 2012) The original report was released by the (the ) and for those of us who are not familiar with his investigation: US. Department of Justice original file After years of investigating, the Department of Justice Thursday. The report is nearly pages and covers subjects ranging from questions about interference in the 2016 to whether President obstructed justice. , taken from . released a redacted copy of special counsel Robert Mueller’s report 400 Russian US presidential election Donald Trump CNN this article For more information about Mueller report, you can either do your own research or check this . Wikipedia page The issue with the original PDF file is that it’s not really a PDF! It’s a scanned file! Section 508 requires your PDF to be accessible to users of assistive technology — like screen readers or Braille displays. Not sure whether it’s a federal crime to release a PDF like that or what you can make of this message by Dept. of Justice (maybe if you email them and ask they actually send you the real version): The Department recognizes that these documents may not yet be in an accessible format. If you have a disability and the format of any material on the site interferes with your ability to access some information, please email the Department of Justice webmaster. To enable us to respond in a manner that will be of most help to you, please indicate the nature of the accessibility problem, your preferred format (electronic format (ASCII, etc.), standard print, large print, etc.), the web address of the requested material, and your full contact information, so we can reach you if questions arise while fulfilling your request. Regardless, the everyone has faced and been working on was to make this PDF file searchable/selectable by going through a series of OCR tools and techniques. Though, depending on the quality, size, and resolution this may not be as accurate as one can hope. In addition, the parts can contribute to some meaningless texts. first challenge redacted : We’ll get back to this issue in a minute. First, let’s have a look at our chosen open-source NLP library to process the Mueller Report file. NOTE Spark NLP by John Snow Labs What is Spark NLP? Spark NLP is a library built on top of and its library. It provides simple, performant and accurate annotations for pipelines, that easily in a environment. text processing Apache Spark Spark ML NLP machine learning scale distributed There are some eye-catching phrases that got my attention the first time I read an article on introducing about a year ago. I love Apache Spark and I learned Scala (and still learning) just for that purpose. Back then I wrote my own Stanford CoreNLP wrapper for Apache Spark. I wanted to stay in the Scala ecosystem so I avoided Python libraries such as spaCy, NLTK, etc. Databricks Spark NLP However, I faced many issues since I was dealing with large-scale datasets. Also, I couldn’t seamlessly integrate my NLP codes into Spark ML pipelines. I can sum up my problems by quoting some parts from the same blog post: Any integration between the two frameworks (Spark and another library) means that every object has to be serialized, go through inter-process communication in both ways, and copied at least twice in memory. We see the same issue when using spaCy with Spark: Spark is highly optimized for loading & transforming data, but running an NLP pipeline requires copying all the data outside the optimized format, serializing it, pushing it to a Python process, running the NLP pipeline (this bit is lightning fast), and then re-serializing the results back to the JVM process. Tungsten This naturally kills any performance benefits you would get from Spark’s caching or execution planner, requires at least twice the memory, and doesn’t improve with scaling. Using CoreNLP eliminates the copying to another process, but still requires copying all text from the data frames and copying the results back in. So I was really excited when I saw there was an NLP library built on top of Apache Spark and it natively extends the Spark ML Pipeline. I could finally build NLP pipelines in Apache Spark! is open source and has been released under the license It is written in but it supports and as well. It has no dependencies on other NLP or ML libraries. Spark NLP’s annotators provide rule-based algorithms, machine learning, and deep learning by using TensorFlow. For a more detailed between Spark NLP and other open source NLP libraries, you can read this . Spark NLP Apache 2.0 . Scala Java Python comparison blog post As a native extension of the , the library offers the capability to train, customize and save models so they can run on a cluster, other machines or saved for later. It is also easy to extend and customize models and pipelines, as we’ll do here. Spark ML API The library covers many , such as: NLP tasks Tokenizer Normalizer Stemmer Lemmatizer RegexMatcher TextMatcher Chunker DateMatcher SentenceDetector DeepSentenceDetector POSTagger ViveknSentimentDetector SentimentDetector: Sentiment analysis Word Embeddings NER CRF NER DL Norvig SpellChecker Symmetric SpellChecker Dependency Parser Typed Dependency Parser For the full list of annotators, models, and pipelines you can read their . online documentation : I am one of the ! Full disclosure contributors Installing Spark NLP My Environments: 2.0.3 release Spark NLP 2.4.1 Apache Spark release 0.8.2 Apache Zeppelin Local setup with MacBook Pro/macOS Cluster setup by Cloudera/CDH 6.2 with 40 servers Programming language: Scala (but no worries, the Python APIs in Spark and Spark NLP are very similar to the Scala language) I will explain how to set up Spark NLP for my environment. Nevertheless, if you wish to try something different you can always find out more about how to use Spark NLP either by visiting the main public repository or have look at their showcase repository with lots of examples: public repository: Main JohnSnowLabs/spark-nlp repository: Showcase JohnSnowLabs/spark-nlp-workshop Let’s get started! To use Spark NLP in Apache Zeppelin you have two options. Either use or you can build a Fat JAR yourself and just load it as an inside Spark session. Why don’t I show you both? Spark Packages external JAR First, with Spark Package: Either add this to your conf/zeppelin-env.sh # set options to pass spark-submit command export = SPARK_SUBMIT_OPTIONS "--packages com.johnsnowlabs.nlp:spark-nlp_2.11:2.0.3" 2. Or, add it to (at the beginning of your notebook before starting your Spark Session): Generic Inline ConfInterpreter %spark.conf # spark.jars.packages can be used adding packages into spark interpreter spark.jars.packages com.johnsnowlabs.nlp:spark-nlp_2 : for .11 2.0 .3 Second, loading an external JAR: To build a Fat JAR all you need to do is: $ git clone https: $ cd spark-nlp $ sbt assembly //github.com/JohnSnowLabs/spark-nlp Then you can follow one of the two ways I mentioned to add this external JAR. You just need to change “ — packages” to “ — jars” in the first option. Or for the second solution, just have “spark.jars”. Start Spark with Spark NLP Now we can start using with and by importing Spark NLP annotators: Spark NLP 2.0.3 Zeppelin 0.8.2 Spark 2.4.1 com.johnsnowlabs.nlp.base._ com.johnsnowlabs.nlp.annotator._ org.apache.spark.ml. import import import Pipeline Apache Zeppelin is going to start a new Spark session that comes with Spark NLP regardless of whether you used Spark Package or an external JAR. Read the Mueller Report PDF file Remember the issue about the PDF file not being a real PDF? Well, we have 3 options here: You can either use any OCR tools/libraries you prefer to generate a PDF or a Text file. Or you can use already searchable and selectable PDF files created by the community. Or you can just use Spark NLP! Spark NLP comes with an package that can read both PDF files and scanned images. However, I mixed option 2 with option 3. (I needed to install Tesseract 4.x+ for image-based OCR on my entire cluster so I got a bit lazy) OCR You can download these two PDF files from Scribd: Mueller Report Redacted Vol I Mueller Report Redacted Vol II Of course, you can just download the Text version and read it by Spark. However, I would like to show you how to use the OCR that comes with Spark NLP. Spark NLP OCR: Let’s create a helper function for everything related to OCR: com.johnsnowlabs.nlp.util.io. ocrHelper = () import OcrHelper val new OcrHelper Now we need to read the PDF and create a Dataset from its content. The OCR in Spark NLP creates one row per page: (DataFrame created by reading the PDF file) As you can see I’m loading the “Volume I” of this report in the format of PDF into a Dataset. I do this locally just to show you don’t always need a cluster to use Apache Spark and Spark NLP! : If the PDF was actually a scanned image, we could have used these settings (but not in our use case, we found a selectable PDF): TIP 1 ocrHelper.setPreferredMethod( ) ocrHelper.setFallbackMethod( ) ocrHelper.setMinSizeBeforeFallback( ) "image" false 0 : You can simply convert Spark Dataset into DataFrame if needed by: TIP 2 muellerFirstVol.toDF() Spark NLP Pipelines and Models NLP by Machine Learning and Deep Learning Now it’s time to do some NLP tasks. As I mentioned at the beginning, we would like to use already pre-trained and provided by Spark NLP in Part I. These are some of the pipelines and models that are available: pipelines models ( Spark NLP pre-trained Pipelines and Models ( full list )) However, I would like to use a pipeline called first. Let’s see how we can download this pipeline, use it to annotate some inputs, and what exactly does it offer: “explain_document_dl” com.johnsnowlabs.nlp.pretrained. pipeline = ( , ) testData = ( ).toDS.toDF( ) pipeline.transform(testData).show import PretrainedPipeline val PretrainedPipeline "explain_document_dl" "en" // This DataFrame has one sentence for testing val Seq "Donald Trump is the 45th President of the United States" "text" // Let's use our pre-trained pipeline to predict the test dataset Here is the result of .show(): ( ) Spark NLP pre-trained “ explain_document_dl ” pipeline I know! It’s a lot going on in this pipeline. Let’s start with NLP annotators we have in pipeline: “explain_document_dl” DocumentAssembler SentenceDetector Tokenizer LemmatizerModel Stemmer PerceptronModel ContextSpellCheckerModel WordEmbeddings (GloVe 6B 100) NerDLModel NerConverter (chunking) To my knowledge, there are some annotators inside this pipeline which are using powered by for their supervised learning. For instance, you will notice these lines when you are loading this pipeline: Deep Learning TensorFlow pipeline: com.johnsnowlabs.nlp.pretrained. = (explain_document_dl,en,public/models) adding (ner-dl/mac/_sparse_feature_cross_op.so,ner-dl/mac/_lstm_ops.so) loading to tensorflow PretrainedPipeline PretrainedPipeline For simplicity, I’ll select a bunch of columns separately so we can actually see some results: ( ) Spark NLP pre-trained “ explain_document_dl ” pipeline So this is a very complete NLP pipeline. It has lots of NLP tasks like other NLP libraries and even more like But, this might be a bit heavy if you are just looking for one or two NLP tasks such as POS or NER. Spell checking. Let’s try another pre-trained pipeline called “entity_recognizer_dl”: com.johnsnowlabs.nlp.pretrained. pipeline = ( , ) testData = ( ).toDS.toDF( ) pipeline.transform(testData).show import PretrainedPipeline val PretrainedPipeline "entity_recognizer_dl" "en" val Seq "Donald Trump is the 45th President of the United States" "text" // Let's use our pre-trained pipeline to predict the test dataset As you can see, using pre-trained pipeline is very easy. You just need to change its name and it will download and cache it locally. What is inside this pipeline? Document Sentence Tokens Embeddings NER NER chunk Let’s walk through what is happening with the NER model in both of these pipelines. The uses ( or ) for training. I can quote one of the main maintainers of the project about what it is: Named Entity Recognition (NER) Word Embeddings GloVe BERT NerDLModel is the result of a training process, originated by NerDLApproach SparkML estimator. This estimator is a TensorFlow DLmodel. It follows a Bi-LSTM with Convolutional Neural Networks scheme, utilizing word embeddings for token and sub-token analysis. You can read this full article about the use of graphs and how Spark NLP uses it to train its NER models: TensorFlow Spark NLP Walkthrough, powered by TensorFlow Back to our pipeline, NER chunk will extract chunks of Named Entities. For instance, if you have -> I-PER and -> I-PER, it will result in . Take a look at this example: Donald Trump Donal Trump ( ) Spark NLP pre-trained “ entity_recognizer_dl ” pipelineCustom Pipelines Personally, I would prefer to build my own NLP pipelines when I am dealing with pre-trained models. This way, I have full control over what types of annotators I want to use, whether I want ML or DL models, use my own trained models in the mix, customize the inputs/outputs of each annotator, integrate Spark ML functions, and so much more! Is it possible to create your own NLP pipeline but still take advantage of pre-trained models? The answer is ! Let’s look at one example: yes document = () .setInputCol( ) .setOutputCol( ) sentence = () .setInputCols( ( )) .setOutputCol( ) .setExplodeSentences( ) token = () .setInputCols( ( )) .setOutputCol( ) normalized = () .setInputCols( ( )) .setOutputCol( ) pos = .pretrained() .setInputCols( , ) .setOutputCol( ) chunker = () .setInputCols( ( , )) .setOutputCol( ) .setRegexParsers( ( )) embeddings = .pretrained() .setOutputCol( ) ner = .pretrained() .setInputCols( , , ) .setOutputCol( ) nerConverter = () .setInputCols( , , ) .setOutputCol( ) pipeline = ().setStages( ( document, sentence, token, normalized, pos, chunker, embeddings, ner, nerConverter )) val new DocumentAssembler "text" "document" val new SentenceDetector Array "document" "sentence" true val new Tokenizer Array "document" "token" val new Normalizer Array "token" "normalized" val PerceptronModel "sentence" "normalized" "pos" val new Chunker Array "document" "pos" "pos_chunked" Array "<DT>?<JJ>*<NN>" val WordEmbeddingsModel "embeddings" val NerDLModel "document" "normalized" "embeddings" "ner" val new NerConverter "document" "token" "ner" "ner_chunked" val new Pipeline Array That’s it! Pretty easy and Sparky. The important part is that you can set which inputs you want for each annotator. For instance, for POS tagging, I can either use tokens, stemmed tokens, lemmatized tokens, or normalized tokens. This can change the results of annotators. Same for NerDLModel. I chose normalized tokens for both POS and Ner models since I am guessing my dataset is a bit messy and requires some cleaning. Let’s use our customized pipeline. If you know anything about Spark ML pipeline, it has two stages. One is fitting which is where you train the models inside your pipeline. The second is predicting your new data by transforming it into a new DataFrame. nlpPipelineModel = pipeline.fit(muellerFirstVol) nlpPipelinePrediction = nlpPipelineModel.transform(muellerFirstVol) val val The .fit() is for decoration here as everything already comes pre-trained. We don’t have to train anything so the .transform() is where we use the models inside our pipeline to create a new DataFrame with all the predictions. But if we did have our own models or Spark ML functions which required training then the .fit() would take some time to train the models. On a local machine, this took about 3 seconds to run. My laptop has a Core i9, 32G Memory, and Vega 20 (if this matters at all) so it is a pretty good machine. ( ) Apache Spark on Local machine This example is nowhere near a scenario where you are dealing with millions of records, sentences, or words. In fact, it’s not even small data. However, we are using Apache Spark for a reason! Let’s run this in a cluster where we can distribute our tasks. Big Data For instance, a while back I had a bigger and much more complicated Spark NLP pipeline to process the entire French Great Debates which is called “ ”. Le Grand Débat Nationale ( ) Politoscope project: https://politoscope.org/2019/03/gdn-preliminaires/ In the end, I was able to have my Spark NLP pipeline in a cluster with over millions of sentences generated by over 250 thousand users. These types of NLP projects are very hard to almost impossible when you are stuck in one machine. ! All we need to do in the cluster is to repartition the DataFrame from 1 partition (since it’s 1 file) to something like 60 (depending on how many executors, cores per executor, etc.). This way Spark can distribute the tasks to more executors and run them in parallel: Back to our own demo muellerFirstVol.rdd.getNumPartitions newMuellerFirstVolDF = muellerFirstVol.repartition( ) nlpPipelineModel = pipeline.fit(newMuellerFirstVolDF) nlpPipelinePrediction = nlpPipelineModel.transform(newMuellerFirstVolDF) val 60 //Now this runs in parallel val val : The reason I created a new DataFrame is, the RDDs are immutable by nature. So you can’t just change their number of partitions. However, you can create a new RDDs (DataFrame) with a different number of partitions. NOTE This time it took to run the pipeline on a cluster. Maybe a few seconds faster in one job is not something to even notice, but you can apply this to tens of thousands of PDFs or millions of records where we can take advantage of Apache Spark distributed engine. 0.4 seconds instead of 3 seconds ( ) Apache Spark on a cluster Now let’s have a look at the results of our customized pipelines. What I would like to do, is a simple grouping on chunks from NER model: ( Spark NLP: NER chunking on Mueller ReportI) As you can see it needs some data cleaning to exclude mistaken entities such as “P.” which we will do in the second part. If we create a co-occurrence matrix of these named entities in the first Volume of Mueller Report we can visualize them in Gephi as I will explain how in the 3rd part: (Spark NLP: Mueller Report Named Entities co-occurrence graph) What comes next: Congratulations! Now you know how to use Spark NLP pre-trained pipelines and models to perform NLP tasks in Apache Spark. This gives you the advantage of a distributed engine in Apache Spark to run large-scale NLP jobs over thousands of cores of CPU. Keep in mind this was a very fast and easy way to start with Spark NLP. In the next part, I would like to experiment with a NER model that is trained by BERT word embeddings instead of GloVe, training my own POS tagger model in Spark NLP from Universal Dependency, run some data cleanings, and finally extract some keywords/phrases by POS and NER chunking. Resources: Spark NLP on GitHub Spark NLP Website Spark NLP examples Spark NLP Slack Spark NLP vs. other NLP libraries aCy Spark NLP vs sp Spark NLP powered by TensorFlow Question? Please leave a comment here or tweet at me on if you have any question. Twitter Found this article interesting? Please follow me ( ) on Medium for future articles and please share! 👏 Maziyar Panahi
