Does Form Recognizer has the ablity to pre-select/pre-diffrentiate documents before they get worked over by the Form Recognizer recognition tool?
E.g. It would be able to diffrentiate between bills or notes and would only use the recognition tool for bills.
As other answers had suggested, you could implement your own classifier. You could call the model, and exam the confidence score of return value, if it's too low, then it's not the document you want to process.
If you want to build 2 models, one for bills, one for notes, each model extract different sets of key/values, then Form Recognize team is working on a feature to find the most appropriate form for you, please stay tuned.
thanks
-xin (from MSFT Form Recognize Team)
Azure Form recognizer is a cognitive service that uses machine learning technology to identify and extract text, key/value pairs and table data from form documents, whether they are PNG, JPEG, TIFF or PDF.
With Form recognizer, You cannot find the type of the document or differentiate document. You need to train any type of form before the extraction of data.
For the purpose of segregation, you have to use some other service/tool. For example, Azure Computer Vision service can be used to find the type of image.
No, that's not a feature from Form Recognizer.
You can implement your own classifier (for images documents) using Custom Vision for example
Related
I am using two different entity extraction methods (https://rasa.com/docs/nlu/entities/) while building my NLP model in the RASA framework to build a chatbot.
The bot should handle different questions which have custom entities as well as some general ones like location or organisation.
So I use both components ner_spacy and ner_crf to create the model. After that I build a small helper script in python to evaluate the model performance. There I noticed that the model struggles to choose the correct enity.
For example for a word 'X' it choosed the pre-defined enity 'ORG' from SpaCy, but it should be recogniced as a custom enity which I defined in the training data.
If I just use the ner_crf extractor I face huge problems in identifing location enities like capitals. Also one of my biggest problems are single answer enities.
Q : "What´s your favourite animal?"
A : Dog
My model is not able to extract this single entity 'animal' for this single answer. If I answer this question with two words like 'The Dog', the model has no problems to extract the animal entity with the value 'Dog'.
So my question is, is it clever to use two different components to extract entities? One for custom enities and the other one for pre-defined enities.
If I use two methods, what´s the mechanism in the model which extractor is used?
By the way, currently I´m just testing things out, so my training samples are not that huge it should be (less then 100 examples). Could the problem been solved if I have much more training examples?
You are facing 2 problems here. I am suggesting few ways that i found helpful.
1. Custom entity recognition:
To solve this you need to add more training sentences with all possible lengths of entities. ner_crf is going to predict better when there are identifiable markers around entities (e.g. prepositions)
2. Extracting entities from single word answer :
As a workaround, i suggest you to do below manipulations on client end.
When you are sending question like What´s your favorite animal?, append a marker to question to indicate to client that a single answer is expected. e.g.
You can send ##SINGLE## What´s your favorite animal? to client.
Client can remove the ##SINGLE## from question and show it to user. But when client sends user's response to server, it doesn't send Dog, it send something like User responded with single answer as Dog
You can train your model to extract entities from such an answer.
Example:
String 1: Help me to track calls.
String 2: Assist me in call tracking.
These two strings have the same meaning but are not identical. Is there any way to find similarity between strings like these using Google Natural Language Processing Api's.
The Google Cloud Natural Language API doesn't provide a specific feature to find similarities between two different strings; instead, this service offers the Content Classification functionality that you can use to classify the strings into categories to then calculate the similarity between them based on their resulting content classification. You can find a helpful Content Classification Tutorial where is explained the process required to perform these tasks.
In case this feature doesn't cover your current needs, you can use the Send Feedback button, located at the lower left and upper right corners of the service public documentation, as well as take a look the Issue Tracker tool in order to raise a Natural Language API feature request and notify to Google about this desired functionality.
i need to develop a chat bot on azure for user interaction i have used LUIS and now want the bot to perform analyze the chat and suggest user the necessary changes. So, should i use text analytic API for it and does LUIS and text analytic API can be used together?
Text analytics can determine sentiments, extract key phrases and detect the language used. If you want to find the intent of the user or extract entities from a text, you can use LUIS.
For "The hotel is the worst ever" a sentiment analysis can tell that the sentiment is negative. For the same sentence key phrase extraction extracts the key words/phrases: "hotel, worst", without any interpretation of the meaning or context.
For "Turn on the yellow light", LUIS can be trained to extract intent (Operate Light) and entities (Action: turn on, Object: Yellow Light) with a meaning and a context.
Text Analytics and LUIS expose separate APIs that just takes texts as input, so they can be used independently of each other. They have no integrations built in between them, so that's up to the consumer to implement.
Is it possible to use DialogFlow to simply parse some text and return the entities within that text?
I'm not interested in a conversation or bot-like behaviour, simply text in and list of entities out.
The entity recognition seems to be better with DialogFlow than Google Natural Language Processing and the ability to train might be useful also.
Cheers.
I've never considered this... but yeah, it should be possible. You would upload the entities with synonyms. Then, remove the "Default Fallback Intent", and make a new intent, called "catchall". Procedurally generate sentences with examples of every entity being mentioned, alone or in combination (in whatever way you expect to need to extract them). In "Settings", change the "ML Settings" so the "ML Classification Threshold" is 0.
In theory, it should now classify every input as "catchall", and return all the entities it finds...
If you play around with tagging things as sys.any, this could be pretty effective...
However, you may want to look into something that is built for this. I have made cool stuff with Aylien's NLP API. They have entity extraction, and the free tier gives you 1,000 hits per day.
EDIT: If you can run some code, instead of relying on SaaS, you could check out Rasa NLU for Entity Extraction. With a SpaCy backend it would do well on recognizing pre-trained entities, and with a different backend you can use custom entities.
I am basically working on nlp, collecting interest based data from web pages.
I came across this source http://schema.org/ as being helpful in nlp stuff.
I go through the documentation, from which I can see it adds additional tag properties to identify html tag content.
It may help search engine to get specific data as per user query.
it says : Schema.org provides a collection of shared vocabularies webmasters can use to mark up their pages in ways that can be understood by the major search engines: Google, Microsoft, Yandex and Yahoo!
But I don't understand how it can help me being nlp guy? Generally I parse web page content to process and extract data from it. schema.org may help there, but don't know how to utilize it.
Any example or guidance would be appreciable.
Schema.org uses microdata format for representation. People use microdata for text analytics and extracting curated contents. There can be numerous application.
Suppose you want to create news summarization system. So you can use hNews microformats to extract most relevant content and perform summrization onit
Suppose if you have review based search engine, where you want to list products with most positive review. You can use hReview microfomrat to extract the reviews, now perform sentiment analysis on it to identify product has -ve or +ve review
If you want to create skill based resume classifier then extract content with hResume microformat. Which can give you various details like contact (uses the hCard microformat), experience, achievements , related to this work, education , skills/qualifications, affiliations
, publications , performance/skills for performance etc. You can perform classifier on it to classify CVs with particular skillsets
Thought schema.org does not helps directly to nlp guys, it provides platform to perform text processing in better way.
Check out this http://en.wikipedia.org/wiki/Microformat#Specific_microformats to see various mircorformat, same page will give you more details.
Schema.org is something like a vocabulary or ontology to annotate data and here specifically Web pages.
It's a good idea to extract microdata from Web pages but is it really used by Web developper ? I don't think so and I think that the majority of microdata are used by company such as Google or Yahoo.
Finally, you can find data but not a lot and mainly used by a specific type of website.
What do you want to extract and for what type of application ? Because you can probably use another type of data such as DBpedia or Freebase for example.
GoodRelations also supports schema.org. You can annotate your content on the fly from the front-end based on the various domain contexts defined. So, schema.org is very useful for NLP extraction. One can even use it for HATEOS services for hypermedia link relations. Metadata (data about data) for any context is good for content and data in general. Alternatives, include microformats, RDFa, RDFa Lite, etc. The more context you have the better as it will turn your data into smart content and help crawler bots to understand the data. It also leads further into web of data and in helping global queries over resource domains. In long run such approaches will help towards domain adaptation of agents for transfer learning on the web. Pretty much making the web of pages an externalized unit of a massive commonsense knowledge base. They also help advertising agencies understand publisher sites and to better contextualize ad retargeting.