1. A method for extracting subject-verb-object (SVO) chunked text from unstructured text, the method comprising: identifying, by a SVO chunked text computing device, a plurality of part of speech (PoS) tokens in an unstructured text; and determining, by the SVO chunked text computing device, a SVO chunked text directly from the plurality of PoS tokens using a machine learning chunker model, wherein the machine learning chunker model is trained on an SVO annotated training data, wherein the SVO annotated training data comprises a plurality of tokens, a plurality of corresponding PoS tags, and a plurality of corresponding SVO tags, the plurality of corresponding SVO tags comprises one or more of a subject tag, a verb tag, an object tag, or an object-subject tag, and the plurality of corresponding SVO tags is in beginninginside-other (BIO) format, and wherein the SVO annotated training data is generated based on a plurality of corresponding span information for the plurality of tokens by for each of a plurality of PoS tokens in each of a plurality of sets of syntactically related PoS tokens in a sentence, detecting a span information for a PoS token and tagging the PoS token as a subject, a verb, an object, or an object-subject based on the span information and a pervious tagging of the PoS token.
1. A method for extracting subject-verb-object (SVO) chunked text from unstructured text, the method comprising: identifying, by a SVO chunked text computing device, a plurality of part of speech (PoS) tokens in an unstructured text; and determining, by the SVO chunked text computing device, a SVO chunked text directly from the plurality of PoS tokens using a machine learning chunker model, wherein the machine learning chunker model is trained on an SVO annotated training data, wherein the SVO annotated training data comprises a plurality of tokens, a plurality of corresponding PoS tags, and a plurality of corresponding SVO tags, the plurality of corresponding SVO tags comprises one or more of a subject tag, a verb tag, an object tag, or an object-subject tag, and the plurality of corresponding SVO tags is in beginninginside-other (BIO) format, and wherein the SVO annotated training data is generated based on a plurality of corresponding span information for the plurality of tokens by for each of a plurality of PoS tokens in each of a plurality of sets of syntactically related PoS tokens in a sentence, detecting a span information for a PoS token and tagging the PoS token as a subject, a verb, an object, or an object-subject based on the span information and a pervious tagging of the PoS token. 4. The method of claim 1 , wherein the machine learning chunker model is trained on one or more of: a non-overlapping SVO annotated training data comprising one set of subject, verb, and object in each of the sentences; or an overlapping SVO annotated training data comprising one or more sets of subject, verb, object, and object-subject in each of the sentences.