Supplemental Material for Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks
نویسندگان
چکیده
منابع مشابه
A Tutorial on Probabilistic Latent Semantic Analysis
Historically, many believe that these three papers [7, 8, 9] established the techniques of Probabilistic Latent Semantic Analysis or PLSA for short. However, there also exists one variant of the model in [11] and indeed all these models were originally discussed in an earlier technical report [10]. In [2], the authors extended MLE-style estimation of PLSA to MAP-style estimations. A hierarchica...
متن کاملLatent Semantic Analysis (Tutorial)
We will see that the number of eigenvalues is n for an n× n matrix. Regarding eigenvectors, if x is an eigenvector then so is ax for any scalar a. However, if we consider only one eigenvector for each ax family, then there is a 1-1 correspondence of such eigenvectors to eigenvalues. Typically, we consider eigenvectors of unit length. Diagonal matrices are simple, the eigenvalues are the entries...
متن کاملAnalyzing Social Networks on the Semantic Web
The past year has seen a dramatic increase in the amount of social information published in RDF documents. Our investigations [1, 2] show that the Friend of a Friend (FOAF) ontology [3] is among the most used semantic web ontologies. This is true if we measure the number of semantic web documents (SWDs) that use the FOAF namespace, as Table I shows, or the number of triples using FOAF terms. Th...
متن کاملQuery expansion based on relevance feedback and latent semantic analysis
Web search engines are one of the most popular tools on the Internet which are widely-used by expert and novice users. Constructing an adequate query which represents the best specification of users’ information need to the search engine is an important concern of web users. Query expansion is a way to reduce this concern and increase user satisfaction. In this paper, a new method of query expa...
متن کاملVolumetric Semantic Segmentation using Pyramid Context Features Supplemental Material
Our “pyramid filtering” insight enables exact and extremely efficient per-voxel classification with minimal memory overhead. We will now demonstrate our improvement over existing techniques empirically and theoretically. For an empirical demonstration of efficiency, consider the following two alternatives to pyramid filtering: 1) the “sliding window” approach: iterate through every voxel in the...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Psychological Methods
سال: 2021
ISSN: ['1082-989X', '1939-1463']
DOI: https://doi.org/10.1037/met0000463.supp