So, "Watch out!" Could become "Be careful"? On Thu, Feb 27, 2020, 10:13 Rajarshi Roychoudhury <rroychoudhu...@gmail.com> wrote:
> It is not just about minimizing loss of sentiment , it is about using > that information for better translation. A very trivial example would be > that for some situations , sentences can project a strong sentiment and > simple translation may not always yield the best result. However if we can > use the knowledge of the sentiment to choose the words , it might give > better result. > > As far as the codes are concerned, I need to study the source code , or a > detailed documentation for proposing a feasible solution. > > Best, > Rajarshi > > > > On Thu, Feb 27, 2020, 23:21 Tino Didriksen <m...@tinodidriksen.com> wrote: > >> My first question would be, is this actually a problem for rule-based >> machine translation? I am not a linguist, but given how RBMT works I can't >> really see where sentiment would be lost in the process, especially >> because Apertium is designed for related languages where sentiment is >> mostly the same. But even for less related languages, it would be down to >> the quality of the source language analysis. >> >> Beyond that, please learn how Apertium specifically works, not just RBMT >> in general. http://wiki.apertium.org/wiki/Documentation is a good start, >> but our IRC channel is the best place to ask technical questions. >> >> One major issue specific to Apertium is that the source information is no >> longer available in the target generation step. >> >> E.g., since you mention English-Hindi, you could install apertium-eng-hin >> and see how each part of the pipe works. We have precompiled binaries >> common platforms. Again, see wiki and IRC. >> >> -- Tino Didriksen >> >> >> On Thu, 27 Feb 2020 at 08:16, Rajarshi Roychoudhury < >> rroychoudhu...@gmail.com> wrote: >> >>> Formally i present my idea in this form: >>> From my understanding of RBMT , >>> >>> The RBMT system contains: >>> >>> - a *SL morphological analyser* - analyses a source language word >>> and provides the morphological information; >>> - a *SL parser* - is a syntax analyser which analyses source >>> language sentences; >>> - a *translator* - used to translate a source language word into the >>> target language; >>> - a *TL morphological generator* - works as a generator of >>> appropriate target language words for the given grammatica information; >>> - a *TL parser* - works as a composer of suitable target language >>> sentences >>> >>> I propose a 6th component of the RBMT system: *sentiment based TL >>> morphological generator* >>> >>> I propose that we do word level sentiment analysis of the source >>> language and targeted language. For the time being i want to work on >>> English-Hindi translation. We do not need a neural network based >>> translation, however for getting the sentiment associated with each word we >>> might use nltk,or develop a character level embedding to just find out the >>> sentiment assosiated with each word,and form a dictionary out of it.I have >>> written a paper on it,and received good results.So basically,during the >>> final application development we will just have the dictionary,with no >>> neural network dependencies. This can easily be done with Python.I just >>> need a good corpus of English and Hindi words(the sentiment datasets are >>> available online). >>> >>> The *sentiment based TL morphological generator *will generate the list >>> of possible words,and we will take that word whose sentiment is closest to >>> the source language word. >>> This is a novel method that has probably not been applied before, and >>> might generate better results. >>> >>> Please provide your valuable feedwork and suggest some necessary changes >>> that needs to be made. >>> Best, >>> Rajarshi >>> >> _______________________________________________ >> Apertium-stuff mailing list >> Apertium-stuff@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/apertium-stuff >> > _______________________________________________ > Apertium-stuff mailing list > Apertium-stuff@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/apertium-stuff >
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