Improvement on Corpus-Based Word Similarity Using Vector-Space Models

Date

2009-09-16

Author

ESİN, yunus emre
ALAN, özgür
Alpaslan, Ferda Nur

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

77
views

0
downloads

This paper presents a new approach for finding semantically similar words from large text collection using window based context methods. Previous studies on this problem mainly concentrate on finding new methods which are new combination of distance-weight measurement methods or new context methods. The main difference of our approach is that we focus on reprocessing of existing methods' outputs to update the representation of related_word vectors, which are used for measuring semantic distance between words, to further improve the results. This new approach can be easily applied to many of the existing word similarity methods using the vector space model for representing contexts. We claim that our method improves the performance of some of the existing similarity measuring methods.

URI

https://hdl.handle.net/11511/38691

DOI

https://doi.org/10.1109/iscis.2009.5291827

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Improvement of corpus-based semantic word similarity using vector space model Esin, Yunus Emre; Alpaslan, Ferda Nur; Department of Computer Engineering (2009) This study presents a new approach for finding semantically similar words from corpora using window based context methods. Previous studies mainly concentrate on either finding new combination of distance-weight measurement methods or proposing new context methods. The main di fference of this new approach is that this study reprocesses the outputs of the existing methods to update the representation of related word vectors used for measuring semantic distance between words, to improve the results further. ...
Comparison of feature-based and image registration-based retrieval of image data using multidimensional data access methods Arslan, Serdar; Yazıcı, Adnan; Sacan, Ahmet; Toroslu, İsmail Hakkı; Acar, Esra (Elsevier BV, 2013-07-01) In information retrieval, efficient similarity search in multimedia collections is a critical task In this paper, we present a rigorous comparison of three different approaches to the image retrieval problem, including cluster-based indexing, distance-based indexing, and multidimensional scaling methods. The time and accuracy trade-offs for each of these methods are demonstrated on three different image data sets. Similarity of images is obtained either by a feature-based similarity measure using four MPEG-...
Implementation Studies of Robot Swarm Navigation Using Potential Functions and Panel Methods Merheb, Abdel-Razzak; GAZİ, VEYSEL; Sezer Uzol, Nilay (2016-10-01) This paper presents a practical swarm navigation algorithm based on potential functions and properties of inviscid incompressible flows. Panel methods are used to solve the flow equations around complex shaped obstacles and to generate the flowlines, which provide collision-free paths to the goal position. Safe swarm navigation is achieved by following the generated streamlines. Potential functions are used to achieve and maintain group cohesion or a geometric formation during navigation. The algorithm is i...
Object-based image labeling through learning by example and multi-level segmentation Xu, Y; Duygulu, P; Saber, E; Tekalp, AM; Yarman Vural, Fatoş Tunay (Elsevier BV, 2003-06-01) We propose a method for automatic extraction and labeling of semantically meaningful image objects using "learning by example" and threshold-free multi-level image segmentation. The proposed method scans through images, each of which is pre-segmented into a hierarchical uniformity tree, to seek and label objects that are similar to an example object presented by the user. By representing images with stacks of multi-level segmentation maps, objects can be extracted in the segmentation map level with adequate...
Automatic Detection of Shared Objects in Multithreaded Java Programs Tolubaeva, Munara; Betin Can, Aysu (2008-12-12) This paper presents a simple and efficient automated tool called DoSSO that detects shared objects in multithreaded Java programs. Our main goal is to help programmers see all potentially shared objects that may cause some complications at runtime. This way programmers can implement a concurrent software without considering synchronization issues and then use appropriate locking mechanism based on the DoSSO results. To illustrate the effectiveness of our tool, we have petformed an experiment on a multithrea...

Citation Formats

y. e. ESİN, ö. ALAN, and F. N. Alpaslan, “Improvement on Corpus-Based Word Similarity Using Vector-Space Models,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38691.