I want to make a resume parsing application using stanford-nlp. I read in stanford-nlp customer reads , that stanford-nlp can be used to make a resume parsing application.
What approach should I use to go a head. I tried using Stanford Named Entity Recognizer. It showed all the names in resume but failed at many times in geting surnames.
How can I get skills and experience from the resumes
Stanford NER only retrieves persons, locations and organizations. In order to retrieve skills you should read other methods or re-train the NER. You can use stackoverflow tags as a starting point.
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We've built a system for generating anesthesia records.
We're now trying to model them as FHIR documents.
I understand that a Document (in FHIR terms) is supposed to end up being kind of a self-contained resource.
But, in our case, we have a process where this document will be gradually assembled.
What's the best way to handle this while we're gathering resources before we're ready to create a document.
We want to use FHIR to create and save various resources as we go, and then at the very end, assemble a document.
Assume the following:
A patient
A provider
A health history
Some info about the procedure being performed
An extensive set of vitals observations
An extensive set of drug doses administered
Various procedure, and recovery notes
A final signature by the provider that will "finalize" the report
I understand we can create and save various resources throughout. But we want to kind of keep them all lumped together so we can easily fetch everything related to what will ultimately become that document.
How would this work in terms of RESTful operations?
POST /Bundle of type "document" with a composition as first element (to create document)
Use resulting ID from bundle? Will I also get an ID for the composition?
Then, how do I add/update/remove individual items from the composition? Do i need to do PUTs of the entire composition to add something?
I have entire series of checkpoints every 5 minutes with full vitals (BP, SpO2, Temp, Respiratory rate, etc). Would I first create those observations with a POST, and then do a PUT to update the composition with a reference to them?
As I'm sure you can tell, I just want to understand how FHIR expects you to do this kind of thing in terms of HTTP operations.
Thanks in advance for any guidance!
You'd start by posting a Composition to have a focal point (table of contents) to update as you gather your data. You would then POST your individual Observations, Procedures, etc. and either PUT or PATCH the Composition to add references to the relevant data. Once you've got all of the relevant information gathered and tied into the Composition, you would then generate the document Bundle. You could create the Bundle earlier in the process and update it each time the Composition changes if you wanted to be able to render the draft document using a FHIR document rendering tool, but otherwise there's no real reason for the Bundle to exist until you're ready to lock down the document.
I have a processor that generates time series data in JSON format. Based on the received data I need to make a forecast using machine learning algorithms on python. Then write the new forecast values to another flow file.
The problem is: when you run such a python script, it must perform many massive preprocessing operations: queries to a database, creating a complex data structure, initializing forecasting models, etc.
If you use ExecuteStreamCommand, then for each flow file the script will be run every time. Is this true?
Can I make in NIFI a python script that starts once and receives the flow files many times, storing the history of previously received data. Or do I need to make an HTTP service that will receive data from NIFI?
You have a few options:
Build a custom processor. This is my suggested approach. The code would need to be in Java (or Groovy, which provides a more Python-like experience) but would not have Python dependencies, etc. However, I have seen examples of this approach for ML model application (see Tim Spann's examples) and this is generally very effective. The initialization and individual flowfile trigger logic is cleanly separated, and performance is good.
Use InvokeScriptedProcessor. This will allow you to write the code in Python and separate the initialization (pre-processing, DB connections, etc., onScheduled in NiFi processor parlance) with the execution phase (onTrigger). Some examples exist but I have not personally pursued this with Python specifically. You can use Python dependencies but not "native modules" (i.e. compiled C code), as the execution engine is still Jython.
Use ExecuteStreamCommand. Not strongly recommended. As you mention, every invocation would require the preprocessing steps to occur, unless you designed your external application in such a way that it ran a long-lived "server" component and each ESC command sent data to it and returned an individual response. I don't know what your existing Python application looks like, but this would likely involve complicated changes. Tim has another example using CDSW to host and deploy the model and NiFi to send it data via HTTP to evaluate.
Make a Custom Processor that can do that. Java is more appropriate. I believe you can do pretty much every with Java you just need to find libraries. Yes, there might be some issues with some initialization and preprocessing that can be handled by all that in the init function of nifi that will allow you preserve the state of certain components.
Link in my use case I had to build a custom processor that could take in images and apply count the number of people in that image. For that, I had to load a deep learning model once in the init method and after through on trigger method, it could be taking the reference of that model every time it processes an image.
I am going to build a chat bot from scratch with rasa.The biggest difficulty now is how to automate production training data.Training data includes nlu.md and stories.md .
I have tried rasa-nlu-trainer and Chatito，But there are still a lot of manual operations，If there are tens of thousands of corpora in the future.How to mark the data to make the data meet the data format of nlu.md and stories.md
Is there an automated tool or program to do this? Thanks a lot!
Well, if you're doing anything ML related, your data is the most important thing that you'll need for the model to learn from. And because we want the model to learn from that data, we create the data and then train the model with it. What you're asking for is for something to somehow create the data for it. It's precisely because there doesn't exist anything like that that we create datasets to train the AI on, by ourselves, so that the model learns form it. So, if you automate the data creation process, what do you expect the model to learn?
So, you can't create the data automatically because if that were possible, we would already have had Artificial General Intelligence (AGI) by now.
But if your goal is to just format the data then you can just write a script for that.
I'm using the Stanford Named Entity toolkit with social media streams. However using that huge number of documents/sentences, I need to enhance the running time performance of the recognizer/classifier. I was wondering what are some techniques that I could do in order to solve this problem.
I need to mention that I only need to recognize one class of entities, organization.
corenlp.sh takes a threads parameter
I have inherited a VB6 project written using DAO on largely Access data.
The code is poor and the application often generates a Windows crash (program "has encountered a problem and needs to close") with large sets of data. I suspect large parts need rewriting.
I want to start with using DAO to relate a recordset of invoice headers (which has one record per invoice) and another of invoice lines (which has several records per invoice). Two fields link these recordsets: Date and Reference.
Though I have seen an example of creating a DAO.Relation, I have no idea how to use it and would welcome some advice please.
If you're looking for a feature like .Net hierarchical datasets, there's nothing like that in DAO or ADO.