OPTIMIZING A LANGUAGE MODEL BASED ON A TOPIC OF CORRESPONDENCE MESSAGES
Technology for optimizing a language model based on a topic identified in correspondence messages. The system may continuously or periodically optimize a language model based on topics identified in past correspondence messages or topics anticipated based on an intended recipient of a correspondence message being drafted. The system can operate in combination or conjunction with a language prediction system, such as a next word prediction application used by a virtual keyboard, thus providing improved language prediction for conversations related to identified topics.
1 . A tangible computer-readable storage medium containing instructions for performing a method of optimizing a language model based on a topic identified in correspondence messages, the method comprising:
maintaining correspondence messages,
wherein the correspondence messages have been transferred from a first party to at least one other party;
receiving an indication to optimize a language model,
wherein the language model is to be optimized based at least in part on a topic identified in correspondence messages;
selecting a language model to be optimized;
identifying correspondence messages associated with at least one of the first party or the at least one other party;
determining a topic in the correspondence messages associated with at least one of the first party or the at least one other party;
identifying a word and/or phrase associated with the determined topic;
optimizing the language model,
wherein optimizing the language model includes adjusting a priority in the language model associated with the identified word and/or phrase; and
outputting the language model.
2 . The tangible computer-readable storage medium of claim 1 , wherein the first party is a user of a device operating a language prediction application, and wherein the identified correspondence messages were sent or received by the user.
3 . The tangible computer-readable storage medium of claim 1 , wherein determining a topic in the correspondence messages includes identifying keywords associated with a topic in correspondence messages.
4 . The tangible computer-readable storage medium of claim 1 , wherein determining a topic in the correspondence messages includes comparing, to a threshold value, a frequency that a word or phrase associated with a topic is used.
5 . The tangible computer-readable storage medium of claim 1 ,
wherein the indication to optimize a language model includes information related to a message being drafted by a user, and
wherein the information related to the message being drafted by the user includes an intended recipient of the message being drafted.
6 . The tangible computer-readable storage medium of claim 1 , wherein the indication to optimize a language model includes information related to an intended recipient of the message, and wherein the method further comprises:
determining a second topic based at least in part on the intended recipient of the message; and
identifying a word and/or phrase associated with the determined second topic,
wherein optimizing the language model further includes adjusting a priority in the language model associated the identified word and/or phrase associated with the determined second topic.
7 . The tangible computer-readable storage medium of claim 1 , wherein the method further comprises:
determining that the topic is no longer active; and
adjusting the priority in the language model associated with the identified word and/or phrase to a previous priority level.
8 . The tangible computer-readable storage medium of claim 1 , wherein the indication to optimize a language model is generated by a language prediction application operating on a device.
9 . A system for optimizing a language model based on a topic identified in correspondence messages, the system comprising:
a memory containing computer-executable instructions of:
a message filtering module configured to:
maintain correspondence messages,
wherein the correspondence messages have been transferred from a first party to at least one other party;
identify correspondence messages associated with at least one of the first party or the at least one other party;
a message analysis module configured to:
determine, in the correspondence messages, a topic associated with at least one of the first party or the at least one other party;
identify a word and/or phrase associated with the determined topic;
a language model identification module configured to select a language model to be optimized; and
a language model optimization module configured to:
receive an indication to optimize a language model,
wherein the language model is to be optimized based at least in part on a topic identified in the identified correspondence messages;
optimize the language model,
wherein the language model is optimized by adjusting a priority in the language model associated with the identified word and/or phrase associated with the determined topic; and
output the language model; and
a processor for executing the computer-executable instructions stored in the memory.
10 . The system of claim 9 , wherein the first party is a user of a device operating a language prediction application, and wherein the identified correspondence messages were sent or received by the user.
11 . The system of claim 9 , wherein the message analysis module is further configured to determine a topic in the correspondence messages based at least in part on identifying keywords associated with the topic in correspondence messages.
12 . The system of claim 9 , wherein the message analysis module is further configured to determine a topic in the correspondence messages based at least in part on a comparison, to a threshold value, of a frequency that a word or phrase associated with the topic is used.
13 . The system of claim 9 ,
wherein the indication to optimize a language model includes information related to a message being drafted by a user, and
wherein the information related to the message being drafted by the user includes an intended recipient of the message being drafted.
14 . The system of claim 9 , wherein the indication to optimize a language model includes information related to an intended recipient of the message, and wherein:
the message analysis module is further configured to determine a second topic based at least in part on the intended recipient of the message; and
identify a word and/or phrase associated with the determined second topic,
wherein the language model optimization module is further configured to optimize the language model by adjusting a priority in the language model associated the identified word and/or phrase associated with the determined second topic.
15 . The system of claim 9 , wherein the message analysis module is further configured to determine that the topic is no longer active; and the language model optimization module is further configured to adjust the priority in the language model associated with the identified word and/or phrase to a previous priority level.
16 . The system of claim 9 , wherein the indication to optimize a language model is generated by a language prediction application operating on a device.
17 . A computer-implemented method for optimizing a language model based on a topic anticipated in a correspondence message being drafted, the method performed by a processor executing instructions stored in a memory, the method comprising:
receiving an indication to optimize a language model,
wherein the language model is to be optimized based at least in part on an anticipated topic of a correspondence message being drafted,
wherein the indication to optimize the language model includes an intended recipient of the correspondence message;
selecting a language model to be optimized;
determining an anticipated topic based at least in part on the intended recipient of the correspondence message;
identifying a word and/or phrase associated with the determined topic;
optimizing the language model,
wherein optimizing the language model includes adjusting a priority in the language model associated with the identified word and/or phrase; and
outputting the language model.
18 . The method of claim 17 , wherein the intended recipient of the correspondence message is a customer service representative.
19 . The method of claim 17 , further comprising:
identifying a second topic in correspondence messages sent between a user and the intended recipient; and
identifying a word and/or phrase associated with the identified second topic,
wherein optimizing the language model includes adjusting a priority in the language model associated with the identified word and/or phrase.
20 . The method of claim 17 , wherein the indication to optimize a language model is generated by a language prediction application operating on a device.