Using Graphical Models, NaturalText's algorithms can find existing connections and make new connections in the data without supervision. From unstructured text to structured data, algorithm can extract facts, make inferences, draw conclusions and create decisions with accuracy. With clear flow and white box model, accuracy can be understood unlike Deep Learning or traditional machine learning models. Adding new data or extending the algorithm is on incremental basis, which makes retraining and remodeling is a thing of past.
Our Algorithm can generate grammatically correct sentences which is not possible even in sophisticated Deep Learning methods. New sentences generated with accuracy, making the algorithm add lot of creativity in Language Generation. Options like making short sentences ideal for few minutes reading or making to long boring sentences makes the algorithm, act as if it mastered the language.
Correcting existing English or any other language text, is easy now. With NaturalText algorithm, not only creating new documents would be effortless task, it can also aid in filling gaps, in fixing grammar errors in format conversion such PDF to text.
Aligning DNA, Nucleic acids and Protein to search for motifs or related sequences has multiple applications in Bioinformatics. With existing algorithms such as BLAST uses matrix substitution, NaturalText algorithm uses pattern matching to do the same while with high speed and full accuracy.
As shown in the alignment of sequences in Bioinformatics, extending that to other areas is possible.
Getting related words from text is useful in constructing dictionaries, thesaurus and domain specific glossaries. NaturalText Contex2Vec algorithm offers effective way to mine contextually related words from any text whether is structured or unstructured.
Converting words into vectors as in Word2vec with speed and flexibility is possible with Contex2Vec. Unlike other blackbox methods, fine grain controlling of Contex2Vec algorithm makes it to work with getting words based on need. New grouping possible based on-streaming implementation.
NaturalText algorithms offer a way to rank document search results using the information within the documents. As this is done without any external reference, private documents can be ranked efficiently.
NaturalText's Artificial Learning Tool based on multiple algorithms is developed to learn, infer, reason with the facts. It can make implicit facts as explicit and show new facts generated from existing data. Tool can show the existing data in different ways.
NaturalText's state of the art Graphical Framework is the base for multiple tasks including Grammar correction, Bioinformatics, Sequence Prediction.
Developed to mine/extract related words using patterns, NaturalText's Contex2Vec Tool is designed with speed and flexibility in mind.
A New Search Result Ranking Algorithm for private documents
NaturalText's MapReduce Framework used to do preprocessing and pre formating of data.
A pure python based general purpose database acts as a base end for all the tools in NaturalText.
Python, Tornado, Ngnix running on Ubuntu is the basic setup used by NaturalText. Open-source frameworks such as MongoDB, RocksDB, Cassandra and Commercial products as Neo4j, Dato etc were tried to develop algorithms and tools but discontinued after the trail period because of low throughput and very low performance metrics.