Topic Model Based Media Program Genome Generation
US-2015206062-A1 · Jul 23, 2015 · US
US2016259774A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2016259774-A1 |
| Application number | US-201514829804-A |
| Country | US |
| Kind code | A1 |
| Filing date | Aug 19, 2015 |
| Priority date | Mar 2, 2015 |
| Publication date | Sep 8, 2016 |
| Grant date | — |
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An information processing apparatus includes a first extracting unit, a second extracting unit, and a third extracting unit. The first extracting unit applies a topic model to target text information and extracts topic distributions for words constituting the text information. The second extracting unit extracts a first topic for the text information from the topic distributions extracted by the first extracting unit. The third extracting unit extracts a word satisfying a predetermined condition, from at least one word having the first topic, as a context word in the text information. The first topic is extracted by the second extracting unit.
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What is claimed is: 1 . An information processing apparatus comprising: a first extracting unit that applies a topic model to target text information and that extracts topic distributions for words constituting the text information; a second extracting unit that extracts a first topic for the text information from the topic distributions extracted by the first extracting unit; and a third extracting unit that extracts a word satisfying a predetermined condition, from at least one word having the first topic, as a context word in the text information, the first topic being extracted by the second extracting unit. 2 . The information processing apparatus according to claim 1 , further comprising: a fifth extracting unit that applies a topic modeling technique to the target text information and that extracts topic distributions in the text information; a sixth extracting unit that extracts a second topic for the text information from the topic distributions extracted by the fifth extracting unit; and a seventh extracting unit that extracts a word satisfying a predetermined condition, from at least one word having the second topic, as a context word in the text information, the second topic being extracted by the sixth extracting unit. 3 . The information processing apparatus according to claim 1 , further comprising: a fourth extracting unit that extracts, from pieces of text information, words constituting the text information; and a generating unit that applies a topic modeling technique to the words extracted by the fourth extracting unit and that generates the topic model. 4 . The information processing apparatus according to claim 2 , further comprising: a fourth extracting unit that extracts, from pieces of text information, words constituting the text information; and a generating unit that applies a topic modeling technique to the words extracted by the fourth extracting unit and that generates the topic model. 5 . The information processing apparatus according to claim 3 , wherein the generating unit uses, as the pieces of text information, pieces of text information serving as supervised data, and applies a supervised topic modeling technique as the topic modeling technique. 6 . The information processing apparatus according to claim 4 , wherein the generating unit uses, as the pieces of text information, pieces of text information serving as supervised data, and applies a supervised topic modeling technique as the topic modeling technique. 7 . A non-transitory computer readable medium storing a program causing a computer to execute a process comprising: applying a topic model to target text information and extracting topic distributions for words constituting the text information; extracting a first topic for the text information from the extracted topic distributions; and extracting a word satisfying a predetermined condition, from at least one word having the first topic, as a context word in the text information. 8 . An information processing method comprising: applying a topic model to target text information and extracting topic distributions for words constituting the text information; extracting a first topic for the text information from the extracted topic distributions; and extracting a word satisfying a predetermined condition, from at least one word having the first topic, as a context word in the text information.
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