Abstract: Generating a table of contents from a computer document is disclosed. The computer document is converted into a markup language, from which a list of grouped textblocks is generated. Headings are detected from among the list of grouped textblocks. For a grouped textblock, a first vector corresponding to a semantic representation of the grouped textblock and a second vector based on evaluation of pre-defined features in the grouped textblock are generated. Based on the first and second vectors, the grouped textblock is classified as a heading or a plain-text using a trained classifier.

Abstract: Methods, systems, and computer program products for generating an executable code based on a document are disclosed. Rules are identified in a document, the identified rules are translated into encoded rules, and an executable code is generated from the encoded rules. Identification of rules includes splitting a text of the document into a plurality of sentences; and for each sentence of the plurality of sentences, determining whether the sentence corresponds to a rule. Translation of an identified rule into an encoded rule includes extracting, from the identified rule, elements corresponding to predefined categories; determining one or more relationships between the extracted elements; and translating the one or more determined relationships into a structured expression. Generating the executable code from the encoded rules includes translating the structured expression associated with the identified rule into a programming language query.

Abstract: A method for processing an electronic document comprising text is disclosed. The method comprises: splitting the text into at least one sentence, and for each said sentence: associating each word of the sentence with a word-vector; representing the sentence by a sentence-vector, wherein obtaining the sentence-vector comprises computing a weighted average of all word-vectors associated with the sentence; if it is determined that the sentence-vector is associated with a tag in a data set of sentence-vectors associated with tags, obtaining the tag from the database; otherwise, obtaining a tag for the sentence-vector using a classification algorithm; processing the sentence if the tag obtained for the sentence is associated with a predetermined label.

Abstract: A method for operating a real-time time series prediction model includes obtaining a first sequence corresponding to a target time series; obtaining a second sequence corresponding to a peer time series of the target time series; determining whether a predetermined number of time steps have elapsed since a last retraining of the real-time time series prediction model; and if the predetermined number of time steps have elapsed since the last retraining of the time series prediction model, retraining the real-time time series prediction model using a first portion of the first and second sequences; and applying a remaining portion of the first and second sequences to the real-time time series prediction model to predict a next value of the target time series.

Abstract: Generating natural language text from structured data using a fusion model is disclosed. Based on an input dictionary, a first sequence of vectors is generated by a first encoder and a second sequence of vectors is generated by a second encoder. The first and second sequences of vectors are provided to an attention function which generates a modified sequence of vectors. A decoder decodes the modified sequence of vectors to generate a plurality of ordered sequences corresponding to a target natural language sentence. A predetermined number of candidate sentences are determined based on the plurality of ordered sequences and are ranked to select a sentence as the target natural language sentence.

Abstract: Generating a table of contents from a computer document is disclosed. The computer document is converted into a markup language, from which a list of grouped textblocks is generated. Headings are detected from among the list of grouped textblocks. For a grouped textblock, a first vector corresponding to a semantic representation of the grouped textblock and a second vector based on evaluation of pre-defined features in the grouped textblock are generated. Based on the first and second vectors, the grouped textblock is classified as a heading or a plain-text using a trained classifier.

Abstract: Generating natural language text from structured data using a fusion model is disclosed. Based on an input dictionary, a first sequence of vectors is generated by a first encoder and a second sequence of vectors is generated by a second encoder. The first and second sequences of vectors are provided to an attention function which generates a modified sequence of vectors. A decoder decodes the modified sequence of vectors to generate a plurality of ordered sequences corresponding to a target natural language sentence. A predetermined number of candidate sentences are determined based on the plurality of ordered sequences and are ranked to select a sentence as the target natural language sentence.

Abstract: Methods, systems, and computer program products for extracting from a descriptive document the value of a slot associated with a target entity described in the descriptive document are disclosed. The descriptive document is split into a set of sentences, and the sentences are filtered to generate a set of candidate sentences. Candidate sentence-entity pairs are determined from the candidate sentences, where each candidate sentence-entity pair includes a candidate sentence and an associated entity of the same type as the slot. The candidate sentence-entity pairs are compared to a set of gold sentences associated with the slot to calculate a plurality of similarity measures. A candidate sentence-entity pair associated with a maximum similarity measure is determined, and the value of the associated entity of the determined candidate sentence-entity pair is assigned to the slot.

Abstract: Methods, systems, and computer program products for extracting from a descriptive document the value of a slot associated with a target entity described in the descriptive document are disclosed. The descriptive document is split into a set of sentences, and the sentences are filtered to generate a set of candidate sentences. Candidate sentence-entity pairs are determined from the candidate sentences, where each candidate sentence-entity pair includes a candidate sentence and an associated entity of the same type as the slot. The candidate sentence-entity pairs are compared to a set of gold sentences associated with the slot to calculate a plurality of similarity measures. A candidate sentence-entity pair associated with a maximum similarity measure is determined, and the value of the associated entity of the determined candidate sentence-entity pair is assigned to the slot.

Abstract: Methods, systems, and computer program products for generating an executable code based on a document are disclosed. Rules are identified in a document, the identified rules are translated into encoded rules, and an executable code is generated from the encoded rules. Identification of rules includes splitting a text of the document into a plurality of sentences; and for each sentence of the plurality of sentences, determining whether the sentence corresponds to a rule. Translation of an identified rule into an encoded rule includes extracting, from the identified rule, elements corresponding to predefined categories; determining one or more relationships between the extracted elements; and translating the one or more determined relationships into a structured expression. Generating the executable code from the encoded rules includes translating the structured expression associated with the identified rule into a programming language query.

Abstract: A method for processing an electronic document comprising text is disclosed. The method comprises: splitting the text into at least one sentence, and for each said sentence: associating each word of the sentence with a word-vector; representing the sentence by a sentence-vector, wherein obtaining the sentence-vector comprises computing a weighted average of all word-vectors associated with the sentence; if it is determined that the sentence-vector is associated with a tag in a data set of sentence-vectors associated with tags, obtaining the tag from the database; otherwise, obtaining a tag for the sentence-vector using a classification algorithm; processing the sentence if the tag obtained for the sentence is associated with a predetermined label.