Application of Lemmatization and Summarization Methods in Topic Identification Module for Large Scale Language Modeling Data Filtering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F12%3A43915504" target="_blank" >RIV/49777513:23520/12:43915504 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-32790-2_23" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-642-32790-2_23</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-32790-2_23" target="_blank" >10.1007/978-3-642-32790-2_23</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of Lemmatization and Summarization Methods in Topic Identification Module for Large Scale Language Modeling Data Filtering

Popis výsledku v původním jazyce

The paper presents experiments with the topic identification module which is a part of a complex system for acquisition and storing large volumes of text data. The topic identification module processes each acquired data item and assigns it topics from adefined topic hierarchy. The topic hierarchy is quite extensive - it contains about 450 topics and topic categories. It can easily happen that for some narrowly focused topic there is not enough data for the topic identification training. Lemmatizationis shown to improve the results when dealing with sparse data in the area of information retrieval, therefore the effects of lemmatization on topic identification results is studied in the paper. On the other hand, since the system is used for processinglarge amounts of data, a summarization method was implemented and the effect of using only the summary of an article on the topic identification accuracy is studied.

Název v anglickém jazyce

Application of Lemmatization and Summarization Methods in Topic Identification Module for Large Scale Language Modeling Data Filtering

Popis výsledku anglicky

The paper presents experiments with the topic identification module which is a part of a complex system for acquisition and storing large volumes of text data. The topic identification module processes each acquired data item and assigns it topics from adefined topic hierarchy. The topic hierarchy is quite extensive - it contains about 450 topics and topic categories. It can easily happen that for some narrowly focused topic there is not enough data for the topic identification training. Lemmatizationis shown to improve the results when dealing with sparse data in the area of information retrieval, therefore the effects of lemmatization on topic identification results is studied in the paper. On the other hand, since the system is used for processinglarge amounts of data, a summarization method was implemented and the effect of using only the summary of an article on the topic identification accuracy is studied.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/LM2010013" target="_blank" >LM2010013: LINDAT-CLARIN: Institut pro analýzu, zpracování a distribuci lingvistických dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Lecture Notes in Computer Science

ISSN

0302-9743

e-ISSN

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Svazek periodika

7499

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

8

Strana od-do

191-198

Kód UT WoS článku

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EID výsledku v databázi Scopus

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