October 2024
Intermediate to advanced
332 pages
10h
Spanish
Content preview from Análisis de Texto Aplicado con Python
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Capítulo 6. Agrupación por similitud de textos
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¿Qué harías si te entregaran un montón de papeles -recibos, correos electrónicos, itinerarios de viaje, actas de reuniones- y te pidieran que resumieras su contenido? Una estrategia podría ser leer cada uno de los documentos, subrayando los términos o frases más relevantes de cada uno, y luego ordenarlos en montones. Si una pila empieza a ser demasiado grande, puedes dividirla en dos pilas más pequeñas. Una vez que hayas revisado todos los documentos y los hayas agrupado, podrías examinar cada montón más detenidamente. Tal vez podrías utilizar las frases o palabras principales de cada montón para redactar los resúmenes y dar a cada uno un nombre único: el tema del montón.
De hecho, es una tarea que se practica en muchas disciplinas, desde la medicina al derecho. En esencia, esta tarea de clasificación se basa en nuestra capacidad para comparar dos documentos y determinar su similitud. Los documentos que son similares entre sí se agrupan y los grupos resultantes describen a grandes rasgos los temas, tópicos y patrones generales dentro del corpus. Esos patrones pueden ser discretos (por ejemplo, cuando los grupos no se solapan en absoluto) o difusos (por ejemplo, cuando hay mucha similitud y los documentos son difíciles de distinguir). En cualquier caso, los grupos resultantes representan un modelo del contenido de todos ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.