Why To Build Your Own Natural Language Processor

The first question to ask yourself is why do I want to build my own when there are so many others out there. There are a few reasons why:
  1. Domain Knowledge Bias: A lot of the ones available are built on global shared models and as everyone uses the models it learns the things that they are training it on. For your specific domain you may want to use your own model.  Some providers may allow you to swap out the model in which case you can simply build your own model rather than a full provider.
  2. Language or Culture Bias: Sometime a model does not support your language and you cannot swap out the model or it does support your language but some of cultural nuances are not handled. If it is related to the model then you can simply swap it out but if it is part of the algorithm you cannot.
  3. Algorithm Failure for given scenarios or you need to inject additional rules to provide context and the processor does not allow for it.
  4. Cost: Using your own means you can host it wherever and control the cost. This can be beneficial if you have high volumes of traffic and there is not an open sourced library you can use that will suit your purposes.
For DAIN and DIANA it was all 4. As they are mentoring we needed to swap out the models. For language there were issues with cultural nuances where words meant multiple things and existing algorithms did not understand.  We also had the Reputation Engine which could add extra context to the NLP that could not be injected into the others.

Now that you know why you need to you can read my next article on How To Build Your Own Natural Language Processor which is coming next Monday.

If you are interested in learning more about building your own then reach out and let's discuss. We have created a ByoNlpQuickStart that can help you get started. I will be providing a that QuickStart and others to SitecoreDain subscribers soon. If you cannot wait email me at chris.williams@readwatchcreate.com and as a subscriber, I can release an early version to get you started now.

Comments

Post a Comment

Popular posts from this blog

Natural Language Processing

Another key concept to DAIN and DIANA is Natural Language Processing. There are a variety of libraries and algorithms that can convert a sentence or series of statements into their various figures of speech.  DAIN and DIANA use the provider model the same way we did for Language Detection. This allows you to swap out your NLP as well as use multiple with the same idea of providing weighting to determine which provider would be considered more important. Weighting can be a simple number weighting or could include simple or more complex rules like this provider works better for English than it does for french or if this sentence contains these keywords then use this provider over that one. Natural Language Processing Steps There are various phases in Natural Language Processing. This article explains the basic ones.  Some libraries will expose the various steps while others will group them together or have a single method to do all of them: Splitting: This is the proces...
Read more

Providers and Pipeline, Oh Why!!!

I have mentioned to a few people that DAIN and DIANA are built with providers and pipelines. The pipelines. Some providers are weighted while others run conditionally based on rules.  The question I get is often is why not just choose an algorithm and stick with it for simplicity.  There are a few reasons for that: There are 3 reasons to have different providers the first is Bias and the second is Context. Bias In the case of Natural Language Processing, you have heard a lot in the news about bias in AI and ML. Some of that is due to data which is the subject of another article but other times it is that the algorithm has been trained with data that is not specific to your domain and thus can produce bias that is not specific to your data. That is why it is so important to to use multiple algorithms to keep the other in check. You can weight one algorithm higher so it is most likely to produce the result but there are cases where it differs greatly from other algorithms t...
Read more

The Universal Content Processing Engine

I am starting to learn more about Digital Asset Management and Content Processing and automatically processing content to gather Metadata both structured and unstructured.  Through this I have come up with a new self-learning engine called the Universal Content Processing Engine. Whereas currently I take advantage of statically programmed logic on how to process a tweet, make calls to given API based on static pipelines, I am limited to how I am coded to a certain extent although some learning has been happening.  Through the use of UCP Engine, I will start with a core set of knowledge and will build upon that.  I will then ask my maker for more information on how to process different types of data. Sometimes it would be a configuration of an existing component and other times it will be another provider dynamically added to my system. To start I will process content from 3 sources: FILE, STRING, URL. Through supervised learning I will learn about more sources and ...
Read more