Method and system for hybrid entity recognition

Abstract

A hybrid entity recognition system and accompanying method identify composite entities based on machine learning. An input sentence is received and is preprocessed to remove extraneous information, perform spelling correction, and perform grammar correction to generate a cleaned input sentence. A POS tagger tags parts of speech of the cleaned input sentence. A rules based entity recognizer module identifies first level entities in the cleaned input sentence. The cleaned input sentence is converted and translated into numeric vectors. Basic and composite entities are extracted from the cleaned input sentence using the numeric vectors.

1 - 19 . (canceled) 
     
     
         20 . A computer-implemented method comprising:
 receiving an input sentence;   tagging parts of speech (POS) of the input sentence;   identifying first level entities using a first machine learning model and POS information; and   identifying composite entities using a second machine learning model fed with the first level entities,   wherein:
 the second machine learning model is used to create and learn linguistics patterns from the first level entities, 
 each composite entity includes a first level entity with a linguistic pattern, and 
 the identified composite entities include a first composite entity and a second composite entity that have a common first level entity but different linguistic patterns. 
   
     
     
         21 . The computer-implemented method of  claim 20 , wherein the first level entities include one or more of Company, Name, Currency, City, Social Security Number, State, E-Mail Address, Product, Contact, and Postal Index Number (Pin) Code. 
     
     
         22 . The computer-implemented method of  claim 20 , wherein the composite entities include one or more of To Date, From Date, Promo Amount, and Payment Account. 
     
     
         23 . The computer-implemented method of  claim 20 , wherein tagging the POS of the input sentence comprises creating a tag set including at least one or more of nouns, pronouns, adverbs, verbs, and help verbs. 
     
     
         24 . The computer-implemented method of  claim 20 , wherein identifying the composite entities uses one or more of machine learning, memory based learning, computational linguistics, and custom rules. 
     
     
         25 . The computer-implemented method of  claim 20 , further comprising storing one or more of the learned linguistic patterns, information of the first level entities, keywords, and relative proximity information of one of the keywords to one of the first level entities. 
     
     
         26 . The computer-implemented method of  claim 20 , further comprising training the first and second machine learning models based on domain specific knowledge. 
     
     
         27 . The computer-implemented method of  claim 20 , further comprising training the first and second machine learning models based on feedback systems. 
     
     
         28 . The computer-implemented method of  claim 20 , further comprising training the first machine learning model, wherein the training comprises:
 preprocessing the input sentence to generate a cleaned input sentence, and   converting and translating the cleaned input sentence into numeric vectors to identify the first level entities.   
     
     
         29 . A system comprising:
 a processors;   a memory in communication with the processor and comprising instructions which, when executed by the processor, program the processor to:
 receive an input sentence; 
 tag parts of speech (POS) of the input sentence; 
 identify first level entities using a first machine learning model and POS information; and 
 identify composite entities using a second machine learning model fed with the first level entities, 
 wherein:
 the second machine learning model is used to create and learn linguistics patterns from the first level entities, 
 each composite entity includes a first level entity with a linguistic pattern, and 
 the identified composite entities include a first composite entity and a second composite entity that have a common first level entity but different linguistic patterns. 
 
   
     
     
         30 . The system of  claim 29 , wherein the first level entities include one or more of Company, Name, Currency, City, Social Security Number, State, E-Mail Address, Product, Contact, and Postal Index Number (Pin) Code. 
     
     
         31 . The system of  claim 29 , wherein the composite entities include one or more of To Date, From Date, Promo Amount, and Payment Account. 
     
     
         32 . The system of  claim 29 , wherein, to tag the POS of the input sentence, the instructions further program the processor to create a tag set including at least one or more of nouns, pronouns, adverbs, verbs, and help verb. 
     
     
         33 . The system of  claim 29 , wherein identifying the composite entities uses one or more of machine learning, memory based learning, computational linguistics, and custom rules. 
     
     
         34 . The system of  claim 29 , wherein the instructions further program the process to store one or more of the learned linguistic patterns, information of the first level entities, keywords, and relative proximity information of one of the keywords to one of the first level entities. 
     
     
         35 . The system of  claim 29 , wherein the instructions further program the process to train the first and second machine learning models based on domain specific knowledge. 
     
     
         36 . The system of  claim 29 , wherein the instructions further program the process to train the first and second machine learning models based on feedback systems. 
     
     
         37 . The system of  claim 29 , wherein the instructions further program the process to train the first machine learning model, wherein the training comprises:
 preprocessing the input sentence to generate a cleaned input sentence, and   converting and translating the cleaned input sentence into numeric vectors to identify the first level entities.   
     
     
         38 . A computer program product for providing persona-based analysis in a project management system, the computer program product comprising a non-transitory computer-readable medium having computer readable program code stored thereon, the computer readable program code configured to:
 receive an input sentence;   tag parts of speech (POS) of the input sentence;   identify first level entities using a first machine learning model and POS information; and   identify composite entities using a second machine learning model fed with the first level entities,   wherein:
 the second machine learning model is used to create and learn linguistics patterns from the first level entities, 
 each composite entity includes a first level entity with a linguistic pattern, and 
 the identified composite entities include a first composite entity and a second composite entity that have a common first level entity but different linguistic patterns. 
   
     
     
         39 . The computer program product of  claim 38 , wherein the computer readable program code is further configured to train the first and second machine learning models based on domain specific knowledge.