User:Asfrent/MSc Log - Revision history

Asfrent: /* 05.08.2014 */

2014-08-05T22:01:32Z

05.08.2014

Asfrent: /* Log */

2014-08-05T22:00:17Z

Log

Asfrent: /* Log */

2014-08-04T22:28:40Z

Log

Asfrent: /* 27.07.2014 */

2014-07-27T13:15:39Z

27.07.2014

Asfrent: /* 27.07.2014 */

2014-07-27T12:58:53Z

27.07.2014

Asfrent: /* Log */

2014-07-27T12:54:37Z

Log

Asfrent: /* 26.07.2014 */

2014-07-26T20:25:05Z

26.07.2014

Asfrent: /* Log */

2014-07-26T15:28:31Z

Log

Asfrent: /* 24.07.2014 */

2014-07-24T22:10:45Z

24.07.2014

Asfrent: /* 24.07.2014 */

2014-07-24T14:38:47Z

24.07.2014

@@ Line 206: / Line 206: @@
 * Refactored the code for the first type of rules. There are two things missing: genetic algorithm for rule selection and acquisition of rules from gen_rnd_hyp.py. Otherwise, everything seems to work fine.
 * Commited everything into the SVN repo.
+* Thesis can be found [http://swarm.cs.pub.ro/~asfrent/msc/thesis.pdf here].

@@ Line 199: / Line 199: @@
  Disambiguate(C1, C2) :- SelectPart(C1, C2), Test(C2)
  Disambiguate(C1, C3) :- Disambiguate1(C1, C2), Disambiguate2(C2, C3)
+=== 05.08.2014 ===
+* Great day!
+* Spoke to spectie on IRC about MIL / Prolog and come to an agreement on the second type of rules that will be hopefully learnable by feeding [semi]random examples. We'll meet again in London :-).
+* Met with Jim, discussed the structure of the thesis and the demo.
+* Refactored the code for the first type of rules. There are two things missing: genetic algorithm for rule selection and acquisition of rules from gen_rnd_hyp.py. Otherwise, everything seems to work fine.
+* Commited everything into the SVN repo.

@@ Line 185: / Line 185: @@
 * Refactored a bit and commited the ''apertium.pl'' file that contains the MIL learner.
 * Utility for listing all tags from a tagged file.
+=== 28.07.2014 - 03.08.2014 ===
+* Generated random examples from the ambiguous / handtagged files.
+* Random examples are able to produce rules of the kind above because of their simplicity (all rules are of the type concept_X(A) :- PRE/POST_token(A,B), is_Y(B)).
+* After having generated enough random rules, the next step needs to be a filtering of the rules. In order to get an idea of how much can we get out of this combination of rule format / training I implemented a genetic algorithm. The best set of rules has an accuracy for disambiguation alone that is about 67% (91% overall). Note that the "still ambiguous" taggings were randomly disambiguated (I also tried to disambiguate with the baseline, same result - this means the rule based disambiguator incorporates all the "knowledge" of the baseline, since after the rule based one the use of random selection or baseline selection does not produce a significant difference).
+=== 04.08.2014 ===
+* Made a stub of the MSc thesis in order to present a draft structure of it.
+* Designed the new format of the rules (next steps include implementing required metarules into the MIL framework):
+ Disambiguate(C1, C2) :- Test(C1), RemovePart(C1, C2)
+ Disambiguate(C1, C2) :- Test(C1), SelectPart(C1, C2)
+ Disambiguate(C1, C2) :- RemovePart(C1, C2), Test(C2)
+ Disambiguate(C1, C2) :- SelectPart(C1, C2), Test(C2)
+ Disambiguate(C1, C3) :- Disambiguate1(C1, C2), Disambiguate2(C2, C3)

@@ Line 184: / Line 184: @@
 === 27.07.2014 ===
 * Refactored a bit and commited the ''apertium.pl'' file that contains the MIL learner.
+* Utility for listing all tags from a tagged file.

← Older revision		Revision as of 12:58, 27 July 2014
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	=== 27.07.2014 ===		=== 27.07.2014 ===
			* Refactored a bit and commited the ''apertium.pl'' file that contains the MIL learner.

← Older revision		Revision as of 12:54, 27 July 2014
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	concept_n(A) :- pre_token(A,B), is_adj(B).		concept_n(A) :- pre_token(A,B), is_adj(B).

			=== 27.07.2014 ===

@@ Line 177: / Line 177: @@
 === 26.07.2014 ===
 * Added directory for auto generated background knowledge. We will have two types of background knowledge: hand crafted (predicates for processing a context, etc.) and auto generated (predicates that define frequent words or tags to use in the MIL learner).
+* After some trouble with the metagol_d code, I have the first automatically learned rule from a not too great set of examples:
+ FINAL HYPOTHESIS FOR EPISODE: concept_n, BOUND: 1
+ concept_n(A) :- pre_token(A,B), is_adj(B).

@@ Line 174: / Line 174: @@
   %   * Remove metadata, assemble tokens, write result.
 * Fully implemented. One test concept that selects <n> if the previous thing is an unambiguous <adj>.
+=== 26.07.2014 ===
+* Added directory for auto generated background knowledge. We will have two types of background knowledge: hand crafted (predicates for processing a context, etc.) and auto generated (predicates that define frequent words or tags to use in the MIL learner).

@@ Line 138: / Line 138: @@
   %   Token -> [Word, Reading1, Reading2, ...].
   %   Word, Reading -> string codes (list of ints).
+  %
   % Internal representation with metadata:
   %   Stream -> [Token1, Token2, ...].
@@ Line 144: / Line 144: @@
   %   MetaReading -> [Reading, Tags].
   %   Word, Reading -> string codes (list of ints).
+* Short description of how the first version of the disambiguator will work:
   % This disambiguator works by concept identification. Concept identification
   % means the disambiguator tries to identify a certain concept and then removes