# US 10,765,956 B2 · Named entity recognition on chat data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,765,956 B2 - Document ID: US10765956B2 - Status: Granted - Kind: B2 - Application number: 14/990,540 - Filed: 2016-01-07 - Published: 2017-07-13 - Granted: 2020-09-08 - Earliest claimed: 2016-01-07 - Assignees: Machine Zone, Inc. - Applicants: MZ IP Holdings, LLC - Inventors: Nikhil Bojja; Shivasankari Kannan; Pidong Wang - Web page: https://patentgrep.com/patent/US10765956B2 ## CPC - A63F 13/87: Communicating with other players during game play, e.g. by e-mail or chat - G06F 40/216: using statistical methods - G06F 40/284: Lexical analysis, e.g. tokenisation or collocates - G06F 40/295: Named entity recognition - G06F 40/40: Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) - G06F 40/58: Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation - A63F: CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a plurality of word strings in a first language, each received word string comprising a plurality of words, identifying one or more named entities in each received word string using a statistical classifier that was trained using training data comprising a plurality of features, wherein one of the features is a word shape feature that comprises a respective token for each letter of a respective word wherein each token signifies a case of the letter or whether the letter is a digit, and translating the received word strings from the first language to a second language including preserving the respective identified named entities in each received word string during translation. ## Claims ### Claim 1 A method comprising performing by one or more computers: training a statistical classifier to identify named entities using training data comprising a plurality of features, wherein one of the features is a word shape feature that comprises a respective token for each letter of a respective word, the respective token indicating that each letter of the respective word is one of an upper case letter, a lower case letter, and a digit; receiving a plurality of word strings in a first language, each received word string comprising a plurality of words; identifying at least one named entity in each received word string using the trained statistical classifier; and translating the received word strings from the first language to a second language, wherein translating comprises preserving the identified at least one named entity in the first language. ### Claim 2 (depends on 1) The method of claim 1 wherein translating the received word strings from the first language to a second language comprises: for a particular received word string: selecting a respective template in the first language, the respective template comprising one or more placeholders for the identified named entities and having a corresponding translated template in the second language that preserves the placeholders; and translating the particular received word string by substituting its identified named entities in the placeholders in the corresponding translated template in the second language. ### Claim 3 (depends on 2) The method of claim 2 wherein the respective template in the first language further comprises words in the first language that are translated, according to a dictionary, to words in the second language in the corresponding translated template. ### Claim 4 (depends on 3) The method of claim 3 wherein the dictionary comprises: words in the first language; and one or more words in the second language corresponding to each of the words in the first language. ### Claim 5 (depends on 2) The method of claim 2 wherein the respective template in the first language further comprises a particular word which count in the particular received word string exceeds a specified threshold. ### Claim 6 (depends on 1) The method of claim 1, wherein a particular named entity comprises one or more proper nouns. ### Claim 7 (depends on 1) The method of claim 1 wherein the plurality of features further comprises one or more of the following features: a prefix, a suffix, a part-of-speech tag, and a word type. ### Claim 8 (depends on 7) The method of claim 7, wherein the word type feature of a particular word describes whether the word shape feature of the particular word comprises tokens of a same type. ### Claim 9 (depends on 1) The method of claim 1 wherein a particular feature is identified with an n-gram within an m-length window, wherein m is greater than n. ### Claim 10 (depends on 1) The method of claim 1 wherein the statistical classifier is specific to the first language. ### Claim 11 (depends on 1) The method of claim 1 wherein the statistical classifier comprises a conditional random field classifier that is configured to identify at least one named entity in a word string. ### Claim 12 A system comprising one or more computers programmed to perform operations comprising: training a statistical classifier to identify named entities using training data comprising a plurality of features, wherein one of the features is a word shape feature that comprises a respective token for each letter of a respective word, the respective token indicating that each letter of the respective word is one of an upper case letter, a lower case letter, and a digit; receiving a plurality of word strings in a first language, each received word string comprising a plurality of words; identifying at least one named entity in each received word string using the trained statistical classifier; and translating the received word strings from the first language to a second language, wherein translating comprises preserving the identified at least one named entity in the first language. ### Claim 13 (depends on 12) The system of claim 12 wherein translating the received word strings from the first language to a second language comprises: for a particular received word string: selecting a respective template in the first language, the respective template comprising one or more placeholders for the identified named entities and having a corresponding translated template in the second language that preserves the placeholders; and translating the particular received word string by substituting its identified named entities in the placeholders in the corresponding translated template in the second language. ### Claim 14 (depends on 13) The system of claim 13 wherein the respective template in the first language further comprises words in the first language that are translated, according to a dictionary, to words in the second language in the corresponding translated template. ### Claim 15 (depends on 14) The system of claim 14 wherein the dictionary comprises: words in the first language; and one or more words in the second language corresponding to each of the words in the first language. ### Claim 16 (depends on 13) The system of claim 13 wherein the respective template in the first language further comprises a particular word which count in the particular received word string exceeds a specified threshold. ### Claim 17 (depends on 12) The system of claim 12, wherein a particular named entity comprises one or more proper nouns. ### Claim 18 (depends on 12) The system of claim 12 wherein the plurality of features further comprises one or more of the following features: a prefix, a suffix, a part-of-speech tag, and a word type. ### Claim 19 (depends on 18) The system of claim 18, wherein the word type feature of a particular word describes whether the word shape feature of the particular word comprises tokens of a same type. ### Claim 20 (depends on 12) The system of claim 12 wherein a particular feature is identified with an n-gram within an m-length window, wherein m is greater than n. ### Claim 21 (depends on 12) The system of claim 12 wherein the statistical classifier is specific to the first language. ### Claim 22 (depends on 1) The system of claim 1 wherein the statistical classifier comprises a conditional random field classifier that is configured to identify at least one named entity in a word string. ### Claim 23 A storage device having instructions stored thereon that when executed by one or more computers perform operations comprising: training a statistical classifier to identify named entities using training data comprising a plurality of features, wherein one of the features is a word shape feature that comprises a respective token for each letter of a respective word, the respective token indicating that each letter of the respective word is one of an upper case letter, a lower case letter, and a digit; receiving a plurality of word strings in a first language, each received word string comprising a plurality of words; identifying at least one named entity in each received word string using the trained statistical classifier; and translating the received word strings from the first language to a second language, wherein translating comprises preserving the identified at least one named entity in the first language. ### Claim 24 (depends on 23) The storage device of claim 23 wherein translating the received word strings from the first language to a second language comprises: for a particular received word string: selecting a respective template in the first language, the respective template comprising one or more placeholders for the identified named entities and having a corresponding translated template in the second language that preserves the placeholders; and translating the particular received word string by substituting its identified named entities in the placeholders in the corresponding translated template in the second language. ### Claim 25 (depends on 24) The storage device of claim 24 wherein the respective template in the first language further comprises words in the first language that are translated, according to a dictionary, to words in the second language in the corresponding translated template. ### Claim 26 (depends on 25) The storage device of claim 25 wherein the dictionary comprises: words in the first language; and one or more words in the second language corresponding to each of the words in the first language. ### Claim 27 (depends on 24) The storage device of claim 24 wherein the respective template in the first language further comprises a particular word which count in the particular received word string exceeds a specified threshold. ### Claim 28 (depends on 23) The storage device of claim 23, wherein a particular named entity comprises one or more proper nouns. ### Claim 29 (depends on 23) The storage device of claim 23 wherein the plurality of features further comprises one or more of the following features: a prefix, a suffix, a part-of-speech tag, and a word type. ### Claim 30 (depends on 29) The storage device of claim 29, wherein the word type feature of a particular word describes whether the word shape feature of the particular word comprises tokens of a same type.