Term weighting systems are of crucial importance in Information Extraction and Information Retrieval applications. Common approaches to term weighting are based either on statistical or on natural language analysis. In this paper, we present a new algorithm that capitalizes from the advantages of both the strategies by adopting a machine learning approach. In the proposed method, the weights are computed by a parametric function, called Context Function, that models the semantic influence exercised amongst the terms of the same context. The Context Function is learned from examples, allowing the use of statistical and linguistic information at the same time. The novel algorithm was successfully tested on crossword clues, which represent a case of Single-Word Question Answering.
Ernandes, M., Angelini, G., Gori, M., Rigutini, L., Scarselli, F. (2007). An adaptive Context-based algorithm for Term Weighting. Application to Single-Word Question Answering. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (pp.2748-2753). FREIBURG : IJCAI-INT JOINT CONF ARTIF INTELL, ALBERT-LUDWIGS UNIV FREIBURG GEORGES-KOHLER-ALLEE.
An adaptive Context-based algorithm for Term Weighting. Application to Single-Word Question Answering
ERNANDES, MARCO;ANGELINI, GIOVANNI;GORI, MARCO;RIGUTINI, LEONARDO;SCARSELLI, FRANCO
2007-01-01
Abstract
Term weighting systems are of crucial importance in Information Extraction and Information Retrieval applications. Common approaches to term weighting are based either on statistical or on natural language analysis. In this paper, we present a new algorithm that capitalizes from the advantages of both the strategies by adopting a machine learning approach. In the proposed method, the weights are computed by a parametric function, called Context Function, that models the semantic influence exercised amongst the terms of the same context. The Context Function is learned from examples, allowing the use of statistical and linguistic information at the same time. The novel algorithm was successfully tested on crossword clues, which represent a case of Single-Word Question Answering.File | Dimensione | Formato | |
---|---|---|---|
ECAI06_Adaptive context.pdf
non disponibili
Tipologia:
Post-print
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
49.94 kB
Formato
Adobe PDF
|
49.94 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/18219
Attenzione
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo