KYOTO at the NTCIR-12 Temporalia Task: Machine Learning Approach for Temporal Intent Disambiguation Subtask
نویسندگان
چکیده
This paper describes the Kyoto system for Temporal Intent Disambiguation (TID) subtask in the NTCIR-12 Temporal Information Access (Temporalia-2) challenge. The task is to estimate the distribution of temporal intents (Past, Recency, Future, Atemporal) of a given query. We took a supervised machine learning approach, using features of bag of words, POS and word vectors. We also incorporated knowledge about temporal and holiday expressions. Our system resulted in a competitive performance.
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