Changes between Version 2 and Version 3 of en/TopicSimilarity

Timestamp:

Jun 6, 2014, 1:15:44 PM (10 years ago)

Author:

xkocinc

Comment:

--

en/TopicSimilarity

@@ -5 +5 @@
 [[Image(/trac/research/raw-attachment/wiki/en/TopicSimilarity/sim_articles.png)]]
 
- * dif
-ferent machine learning methods as Random Projections, TFIDF word weighting, Latent Semantic Indexing/Analysis, Latent Dirichlet Allocation
+ * different machine learning methods as Random Projections, TFIDF word weighting, Latent Semantic !Indexing/Analysis, Latent Dirichlet Allocation
 
  * 50,000+ fulltexts on http://dml.cz
@@ -18 +17 @@
 Similarity types: from '''plagiarism''' (similarity on n-grams, narrative similarity, evolved into http://theses.cz) to '''thematic, topical similarity'''.
 
-== Prehistoric Example: Project Ottuv Slovnk naucny, 1998 ==
+== Prehistoric Example: Project Ottuv Slovnk naucny, 1998 ==
 
 Levels of content processing: strings -> words and collocations -> semantics (word meaning) -> information (knowledge).
@@ -57 +56 @@
 architecture.
 
-Developed by NLPlab PG student Radim Rehurek (awarded in Ceska hlava competition in 2011).
+Developed by NLPlab PG student Radim Rehurek (awarded in Ceska hlava competition in 2011).
 
 Leading edge machine learning methods implemented. 
@@ -63 +62 @@
 Used in 40+ local, EU or worldwide projects. 
 
-Typical deployment and 
-ne-tuning scenario: expressing data as words (features) -> con
-guration of topic modeling of
+Typical deployment and ne-tuning scenario: expressing data as words (features) -> conguration of topic modeling of
 features -> setting of gensim methods and tuning parameters -> usage in an application with proper vizualization interface.
 
@@ -73 +70 @@
  * similarity: plagiarism
  * topical modeling
- * thematic document 
-ltering
+ * thematic document ltering
  * visualization
  * semantic, meaning computations and modeling of natural language texts
 
 
-Credits: Jiri Franek (illustrations)
+Credits: Jiri Franek (illustrations)