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 <[email protected]> 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 < > [email protected]> 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 > [email protected] > https://lists.sourceforge.net/lists/listinfo/apertium-stuff >
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