User:Shash42/GSoC 2020 Proposal: Bilingual Dictionary Discovery - Revision history

Shash42: /* Phase 3 */

2020-03-31T22:49:15Z

Phase 3

Shash42: /* Phase 3 */

2020-03-31T22:48:37Z

Phase 3

Shash42: /* Phase 3 */

2020-03-31T22:48:09Z

Phase 3

Shash42 at 22:46, 31 March 2020

2020-03-31T22:46:07Z

Shash42: Shash42 moved page User:Shash42/GSoC 2020 Bilingual Dictionary Discovery to User:Shash42/GSoC 2020 Proposal: Bilingual Dictionary Discovery

2020-03-31T22:42:27Z

Shash42 moved page User:Shash42/GSoC 2020 Bilingual Dictionary Discovery to User:Shash42/GSoC 2020 Proposal: Bilingual Dictionary Discovery

Shash42 at 22:41, 31 March 2020

2020-03-31T22:41:28Z

Shash42: Shash42 moved page User:Shash42/GSoC 2020:Bilingual Dictionary Discovery to User:Shash42/GSoC 2020 Bilingual Dictionary Discovery

2020-03-31T22:38:18Z

Shash42 moved page User:Shash42/GSoC 2020:Bilingual Dictionary Discovery to User:Shash42/GSoC 2020 Bilingual Dictionary Discovery

Shash42: Shash42 moved page User:Shash42 to User:Shash42/GSoC 2020:Bilingual Dictionary Discovery

2020-03-31T22:37:18Z

Shash42 moved page User:Shash42 to User:Shash42/GSoC 2020:Bilingual Dictionary Discovery

Shash42 at 17:24, 30 March 2020

2020-03-30T17:24:08Z

Shash42: /* Work done on this problem */

2020-03-30T16:45:01Z

Work done on this problem

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 '''''Deliverable #3'''''
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-An advanced Bilingual Dictionary Generation tool extended for different use cases. Extensive Documentation and Evaluation Report.
+An advanced Bilingual Dictionary Generation tool extended for different use cases. Extensive Documentation and Evaluation Report. <br>
 '''Project Completed'''

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 '''[Extension 2]:'''
-* A major benefit of Bilingual Dictionaries over tools like Crossdics is the possibility that changes to a few entries <be> do not necessitate re-generation of the whole dictionary. Utilize this feature by providing a different mode of input, where only <br> a few additions have to be made to an existing bilingual dictionary, and predictions are made for other pairs accordingly.
+* A major benefit of Bilingual Dictionaries over tools like Crossdics is the possibility that changes to a few entries <br> do not necessitate re-generation of the whole dictionary. Utilize this feature by providing a different mode of input, where only <br> a few additions have to be made to an existing bilingual dictionary, and predictions are made for other pairs accordingly.
 |-

@@ Line 199: / Line 199: @@
 |
 '''[Extension 2]:'''
-* A major benefit of Bilingual Dictionaries over tools like Crossdics is the possibility that changes to a few entries do not necessitate re-generation of the whole dictionary. Utilize this feature by providing a different mode of input, where only a few additions have to be made to an existing bilingual dictionary, and predictions are made for other pairs accordingly.
+* A major benefit of Bilingual Dictionaries over tools like Crossdics is the possibility that changes to a few entries <be> do not necessitate re-generation of the whole dictionary. Utilize this feature by providing a different mode of input, where only <br> a few additions have to be made to an existing bilingual dictionary, and predictions are made for other pairs accordingly.
 |-

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 *'''Name''': Shashwat Goel
 *'''Official Email''': [mailto:shashwat.goel@research.iiit.ac.in shashwat.goel@research.iiit.ac.in]
-*'''IRC''': shash42
+*'''IRC''': shash
 *'''Personal (Google) Email''': [mailto:shashwatnow@gmail.com shashwatnow@gmail.com]
 *'''GitHub''': [https://github.com/shash42 shash42]

← Older revision		Revision as of 22:41, 31 March 2020
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	T. Flati and R. Navigli. "The CQC algorithm: Cycling in graphs to semantically enrich and enhance a bilingual dictionary." Proceedings of the Twenty-Third international joint conference on Artificial Intelligence. AAAI Press, 2013.		T. Flati and R. Navigli. "The CQC algorithm: Cycling in graphs to semantically enrich and enhance a bilingual dictionary." Proceedings of the Twenty-Third international joint conference on Artificial Intelligence. AAAI Press, 2013.


			[[Category:GSoC 2020 student proposals]]

← Older revision	Revision as of 22:42, 31 March 2020
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 <pre>
-For example, consider the English word ‘wrist’. It translates to the Spanish word ‘muñeca’, which in turn translates into French ‘poupée’.
+For example, consider the English word ‘wrist’. It translates to the Spanish word ‘muñeca’,
+which in turn translates into French ‘poupée’.
 It turns out, muñeca is polysemous, or that it takes two semantic meanings, the English words ‘wrist’ and ‘doll’.
 Poupée does actually mean ‘doll’, but a transitive relation would map it with English ‘wrist’.
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 In the coding challenge, I have used formatted RDF Graph inputs from [https://github.com/martavillegas/ApertiumRDF/tree/master/sample%20words ApertiumRDF-Github]. The input consists of “<word><POS><language>” representing each lemma and each line representing a translation between two lemmas, which is an edge. The current algorithm is quite rudimentary, and just a proof of concept. However, it can be seen that it performs well on the example discussed above, unlike Apertium Crossdics. Moreover, the ability to infer using data from multiple languages is shown. The data contains files with up to 13 languages at once.
 <pre>
-False positives due to polysemy are avoided: doll-n-en is not translated to poupée-n-fr.
+-> False positives due to polysemy are avoided: doll-n-en is not translated to poupée-n-fr.
-It is able to infer multiple senses for the same word correctly: pojno-n-en is translated to both wrist-n-en and doll-n-en
+-> It is able to infer multiple senses for the same word correctly:
+pojno-n-en is translated to both wrist-n-en and doll-n-en
-For many words, multiple new translations are correctly inferred: For poignet-n-fr, 4 different translations are inferred. wrist-n-en, canell-n-ca, moneca-n-gl, eskumutur-n-eu
+-> For many words, multiple new translations are correctly inferred: For poignet-n-fr,
+different translations are inferred. wrist-n-en, canell-n-ca, moneca-n-gl, eskumutur-n-eu
 </pre>

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 I have gone through the research papers provided to me by my mentors and compiled notes. I have discussed my observations with them. Some of these are already mentioned in the Proposal section. I have thought about the scope for optimization and read some papers for the same. Most of this has been included in the above proposal.
-I have completed the coding challenge prescribed to me by my mentor Mikel Forcada (mlforcada). It is available at [https://github.com/shash42/ApertiumBidixGen https://github.com/shash42/ApertiumBidixGen].
+I have completed the coding challenge prescribed to me by my mentor Mikel Forcada (mlforcada). It is available at [https://github.com/shash42/ApertiumBidixGen https://github.com/shash42/ApertiumBidixGen]. The entire code has been written by me, and it follows the procedure described by Gracia et al., 2017.
 In the coding challenge, I have used formatted RDF Graph inputs from [https://github.com/martavillegas/ApertiumRDF/tree/master/sample%20words ApertiumRDF-Github]. The input consists of “<word><POS><language>” representing each lemma and each line representing a translation between two lemmas, which is an edge. The current algorithm is quite rudimentary, and just a proof of concept. However, it can be seen that it performs well on the example discussed above, unlike Apertium Crossdics. Moreover, the ability to infer using data from multiple languages is shown. The data contains files with up to 13 languages at once.