Walter Preston Boggess, Jr v. Lawrence Morris, Warden, Utah State Prison : Brief of Appellant

Appeal from the Judgment of the Third Judicial District Court, In and for Salt Lake County, State of Utah, the Honor-Able David K. Winder, Judge, Presidin

Machine learning for violence risk assessment using Dutch clinical notes

Algorithms and the Foundations of Software technolog

Word semantic similarity for morphologically rich languages

\u3cp\u3eIn this work, we investigate the role of morphology on the performance of semantic similarity for morphologically rich languages, such as German and Greek. The challenge in processing languages with richer morphology than English, lies in reducing estimation error while addressing the semantic distortion introduced by a stemmer or a lemmatiser. For this purpose, we propose a methodology for selective stemming, based on a semantic distortion metric. The proposed algorithm is tested on the task of similarity estimation between words using two types of corpus-based similarity metrics: co-occurrence-based and context-based. The performance on morphologically rich languages is boosted by stemming with the context-based metric, unlike English, where the best results are obtained by the co-occurrence-based metric. A key finding is that the estimation error reduction is different when a word is used as a feature, rather than when it is used as a target word.\u3c/p\u3