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Head and modifier detection is an important problem for applications that handle short texts such as search queries, ads keywords, titles, captions, etc. In many cases, short texts such as search queries do not follow grammar rules, and existing approaches for head and modifier detection are coarse-grained, domain specific, and/or require labeling of large amounts of training data. In this paper, we introduce a semantic approach for head and modifier detection. We first obtain a large number ofdoi:10.1109/icde.2014.6816658 dblp:conf/icde/WangWH14 fatcat:xjh5vpij4vcvlodiuezruxckxq