Named Entity Recognition

You can instantly and accurately perform entity extraction from text.
Our NER extractor uses state of the art natural language understanding/NLP to give you best extraction results.
It is based on ThatNeedle's proprietary high-precision and high-recall language processing stack.

The online/cloud version is free! No registration needed.
You can pay for an offline version that you can deploy on your server.


   
$3000 $1500
Limited period 50% discount. Use cloud version free here : http://entity.thatneedle.com


Semantic

We will give you semantically better results than other commercial and open-source entity extraction solution in the market today. This is because we are not based on finding keywords and tagging them.
We go deeper into the semantics and understand the intent of the text better than others to give you the best entity recognition.
Blazingly Fast

How fast? Practically instant for most messages!
Want performance figures? 10 milliseconds for processing most sentences. This is 10x faster than other contemporary commercial software solutions available today and is suited for real time natural language processing of text.
Local & Cloud

NEW!! You can now host our entity extracting binaries on your premises!
Hosting on your machines leads to reduced latency and maximum protection of your data.
We also deploy our NER/extractor service via an API in the cloud.
That means you don't need to indulge in setup and infrastructure.
No expertise needed

You don't need to be an expert in Natural language processing (NLP) , machine learning(ML), data science, data modelling, Aritificial intelligence(AI) etc. No need to even get acquianted with all the mumbo-jumbo jargon. Just let us know your business needs and we will custom create exactly the entity extraction you need for your business.




Custom Entity Extraction

Let us know about your custom entity extraction needs. Some solutions restrict the entities to nouns, proper nouns etc. But depending on the business needs, you might want to have some particular types identified and extracted as entities. You should be able to define what to extract as entity and what not to label as an entity. If done naively, this is a tricky exercise and people often end up burning their hands. We will create the best solution for your text analysis and named entity recognition needs. We can custom create and test custom models for your niche and give you the pre-trained software solution that is ready to use for your niche and specific needs.
While the software allows the user to define custom entities and annotation, any other customization cost would be over and above the default price mentioned.
The default language is English, but the technology is capable of effective handling other languages, includes Asian languages like Chinese, Japanese, Arabic etc. These are traditionally a challenge, but our algorithms are designed to solve these language understanding issues.



About Named Entity Recognition

NER or Named Entity Recognition / Entity extraction identifies, extracts and labels the information in text into pre-defined categories, or classes such as location, names of people etc. It is a loosely used term to also include entity-extraction of information such as dates, numbers, phone, url etc. Entities could be any useful data or information for example, date time, names, location etc that could be stored or used for text processing. Some extractors, identify proper nouns or nouns as identities but thats too rigid and is not a good rule. A good entity extractor should be able to take a string of unstructured text and identify and produce annotated output that helps in intelligent and better analysis of the text. Such intelligent understanding of the intent of the user query will help in producing better responses from the system. If it is a search query, it would mean better and more relevant search results. There is no universal entity extractor and the needs of the business must be taken into account before selecting a software tool to perform such tasks. Many such general purpose tools give poor accuracy for the context of the business in question, and are therefore not fit to be used in specific niches. A good tool will recognize the context of the niche and give annotations and analysis accordingly.

Importance of fast entity extraction from natural language

Most language processing software cannot parse the query and analyse it fast enough to be used effectively in user interfacing applications. As a result response from the backend system appears to be slow and tests the patience of the end user. A fast response is essential not only to delight the customer, but to keep him engaged. A slow application will give the user a good reason to direct his valuable attention elsewhere. ThatNeedle has always recognised the need for speed in NLP and is making the core engine faster everyday. We are also proud to say that we are 10x faster than some leading entity extraction service providers such as Microsoft(LUIS). [As benchmarked in August 2017] This would make ThatNeedle an ideal candidate for real time extraction tasks from plain text. ThatNeedle NER can serve as an ideal text processing tool for big data scientists, data architects, semantic search solution providers, realtime natural language processing, large scale NLP etc

Out of the box, ThatNeedle could be used as an effective and faster alternative to Microsoft Luis, IBM watson, Wit.ai, api.ai etc

Even if you are using traditional specialized parsers like Natty for Java or any similar library for date extraction etc, you should compare the performance with ThatNeedle and decide for yourself!

How to select an entity extraction tool / software / framework

There a many software tools for NLP floating around in the market. Some are just repackaging open source software, some are repackaging white labelleled software. There are many open source NER tools, one prominent tool is Stanford NER (in Java). NLTK (Natural Language Tool Kit) is a very popular python library for natural language processing in python. Some tools would require a learning curve and getting familiar with parts of speech tagging and some language processing know how. While the learning curve is a crucial factor in a business decision, another important thing is to test and compare the tools. We encourage you to try out some open source and commercial software so that you can truly appreaciate the value that ThatNeedle brings to the table. See how it performs out of the box for the advertised test cases. You should then vary the test cases. Make a note of the speed and the accuracy of the of software performance. A good tool should also have some features to customize for your niche. There is a fair chance you do not have a good dataset to train a fresh ML system for your niche. Relevant data sets are hard to obtain and one that is sufficiently large in size is rare. While many open source data sets are available, many wont be relevant to you. Integration is also an issue that should be looked into before selecting anything. ThatNeedle was designed to make integration with any technology easy. You could be using python, java, c++, ruby, lisp, erlang, golang, scala, node.js etc and you would still be able to use ThatNeedle entity extractor. What's more the technology is cross platform and would work on your favourite OS. Please confirm once before purchasing.

Input / Output example


Input could be :
blahblah on 28th Sept
The default / "out of the box" output would be a JSON formatted string similar to the one illustrated below (representative only, actual might differ):
{"DATE":"on 28th dec", }
i.e the blahblah would be ignored; however it is possible that blahblah could be an important entity for your special niche. In that case you could custom define blahblah as a special entity named blah_entity. After that, the new special entity would also be recognized and labelled accordingly.

How to use the free online / cloud entity extraction application


The cloud version is free and easy to use. There is no registration required. Just start using it in your applications as illustrated below:
http://entity.thatneedle.com/baggage?q=your query here
Example:
http://entity.thatneedle.com/baggage?q=12th November
returns
{"status":"{DATE:12 november , }"}

Built in entities recognized out of the box by default (No training required)

Information extraction:-

The following information can be extracted by default from the natural language text to better understand the entities, attributes, intents. This can be done without any training of the models.

Natural language date parsing

Natural language time parsing

Natural language price parsing

Natural language numbers parsing