PPT-A New Semantic Similarity Based Measure for Assessing Research Contribution

Petr Knoth amp Drahomira Herrmannova Knowledge Media institute The Open University Current impact metrics Pros simplicity a vailability for evaluation purposes Cons insufficient evidence of quality and research contribution. A Unified Approach for Measuring Semantic Similarity. Mohammad . Taher. . Pilehvar. David . Jurgens. Roberto Navigli. Semantic. . Similarity. ; . how. . similar. are a . pair. of . lexical. . items. Given:. A query image. A database of images with known locations. Two types of approaches:. Direct matching. : directly match image features to 3D points (high memory requirement). Retrieval based. : retrieve a short list of most similar images and perform image matching. . Multi-label Protein Subcellular Localization. Shibiao WAN and Man-Wai MAK. The Hong Kong Polytechnic University. Sun-Yuan KUNG. Princeton University. Outline. Introduction and Motivation. Retrieval of GO Terms. Ciro . Cattuto. , Dominik Benz, Andreas . Hotho. , . Gerd. . Stumme. Presented by. Smitashree. . Choudhury. Overview. Motivation. Measures of . semantic Relatedness. Semantic . Grounding of measures. １２月７日. 研究会. 祭都援炉. (. マットエンロ. ). Up until now: Getting to know NLP. “Speech and Language Processing” (. Jurafsky. & Martin). 論文：. On-Demand Information Extract . S. imilarity to Semantic Relations. Georgeta. . Bordea. , November 25. Based on a talk by Alessandro . Lenci. . titled “Will DS ever become Semantic?”, Jan 2014. Distributional Semantics . (DS. of Binaries. Yaniv David. , Nimrod . Partush. , . Eran. . Yahav. . @. *. The research leading to these results has received funding . from the . European Union's - Seventh Framework . Programme. (FP7) under grant agreement n° . Kai Li, Guo-Jun Qi, Jun Ye, Tuoerhongjiang Yusuph, Kien A. Hua. Department of Computer Science. University of Central Florida. ISM 2016. Presented by . Tuoerhongjiang Yusuph. Introduction. Massive amount of high-dimensional data, high computational costs …. Lioma. Lecture . 18: Latent Semantic Indexing. 1. Overview. Latent semantic indexing . Dimensionality reduction. LSI in information retrieval. 2. Outline. Latent semantic indexing . Dimensionality reduction. Bamshad Mobasher. DePaul University. Distance or Similarity Measures. Many data mining and analytics tasks involve the comparison of objects and determining in terms of their similarities (or dissimilarities). Text Similarity. Motivation. People can express the same concept (or related concepts) in many different ways. For example, “the plane leaves at 12pm” vs “the flight departs at noon”. Text similarity is a key component of Natural Language Processing. Petra Bud. íková, FI MU. CEMI meeting, Plze. ň. , 1. 6. . . 4. . 2014. Formalization. The annotation problem is . defined by a . query image . I. . and a . vocabulary . V. of candidate concepts. Quiz. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. 3. Deer-mouse. 4. Deer-roof. Quiz Answer. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. C. Binding, K. May. 1. , R. Souza, D. Tudhope, A. Vlachidis. Hypermedia Research Unit, University of Glamorgan. 1. English Heritage. STAR Project - Aims. In. vestigate semantic technologies for integrating and cross searching datasets and associated grey literature.