Abstract: Methods, systems, and computer program products for a semi-automatic process for creating a natural language processing resource are provided herein. A computer-implemented method includes identifying multiple annotation tasks in connection with natural language processing of input text, and automatically determining, based on analysis of (i) parameters related to the identified annotation tasks and (ii) parameters related to annotation task users, routing instructions for the identified annotation tasks, wherein the routing instructions comprise (a) instructions to route a first sub-set of the identified annotation tasks to non-expert annotation task users and (b) instructions to route a second sub-set of the identified annotation tasks to expert annotation task users.

Abstract: Methods, systems, and computer program products for a semi-automatic process for creating a natural language processing resource are provided herein. A computer-implemented method includes identifying multiple annotation tasks in connection with natural language processing of input text, and automatically determining, based on analysis of (i) parameters related to the identified annotation tasks and (ii) parameters related to annotation task users, routing instructions for the identified annotation tasks, wherein the routing instructions comprise (a) instructions to route a first sub-set of the identified annotation tasks to non-expert annotation task users and (b) instructions to route a second sub-set of the identified annotation tasks to expert annotation task users.

Abstract: One embodiment provides method for constructing a cross-lingual information extraction program, the method including: utilizing at least one processor to execute computer code that performs the steps of: constructing a plurality of language-specific representations from text expressed in a plurality of languages by parsing the text of each language using a language-specific semantic parser; mapping the plurality of language-specific representations to a single cross-lingual semantic representation, wherein the cross-lingual semantic representation encompasses the plurality of languages; and constructing the cross-lingual information extraction program based on the cross-lingual semantic representation. Other aspects are described and claimed.

Abstract: One embodiment provides a method for generating a natural language resource using a parallel corpus, the method including: utilizing at least one processor to execute computer code that performs the steps of: receiving, from a parallel corpus, natural language text in a source language and a corresponding translation of the natural language text in a target language, wherein the natural language text in the source language comprises linguistic annotations; projecting the linguistic annotations from the source language natural language text to the target language natural language text; applying one or more filters to remove at least one projected linguistic annotation from the target language natural language text that results in at least one error; selecting at least one target language natural language text having substantially complete linguistic annotations; training a machine learning model using the selected at least one target language natural language text and annotations; and adding, using the trained

Abstract: One embodiment provides method for constructing a cross-lingual information extraction program, the method including: utilizing at least one processor to execute computer code that performs the steps of: constructing a plurality of language-specific representations from text expressed in a plurality of languages by parsing the text of each language using a language-specific semantic parser; mapping the plurality of language-specific representations to a single cross-lingual semantic representation, wherein the cross-lingual semantic representation encompasses the plurality of languages; and constructing the cross-lingual information extraction program based on the cross-lingual semantic representation. Other aspects are described and claimed.

Abstract: One embodiment provides a method for generating a natural language resource using a parallel corpus, the method including: utilizing at least one processor to execute computer code that performs the steps of: receiving, from a parallel corpus, natural language text in a source language and a corresponding translation of the natural language text in a target language, wherein the natural language text in the source language comprises linguistic annotations; projecting the linguistic annotations from the source language natural language text to the target language natural language text; applying one or more filters to remove at least one projected linguistic annotation from the target language natural language text that results in at least one error; selecting at least one target language natural language text having substantially complete linguistic annotations; training a machine learning model using the selected at least one target language natural language text and annotations; and adding, using the trained