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.
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.
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.
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!