As I mentioned earlier, I would like to work on English-Hindi or English-Bengali translation, the dataset can be obtained from sentiwordnet for Indian languages, https://amitavadas.com/sentiwordnet.php which is by far the most resourceful dataset available for sentiment analysis.It contains data for both Hindi and Bengali.
I cannot give any example specific to apertium because whenever I try to translate a word from English in the interface, the available languages for translation are beyond my knowledge. I am not sure if I am right, but Hindi/Bengali is probably not one of the languages to which an English word can be translated to. Correct me if I am wrong On Fri, Feb 28, 2020, 00:31 Tanmai Khanna <khanna.tan...@gmail.com> wrote: > Hi, I have a few questions about this: > 1. How would you analyse the sentiment of the source text? Considering the > language pairs that Apertium deals with are low resource languages. > 2. As Tino mentions, is there a problem of sentiment loss in Apertium? Any > examples of this? > 3. Doesn't the sentiment analysis of a language require a decent amount of > training data? Where would this data be found for low resource languages? > > Tanmai > > On Fri, Feb 28, 2020 at 12:02 AM Rajarshi Roychoudhury < > rroychoudhu...@gmail.com> wrote: > >> The effect won't be very evident on simple sentences, I think it would be >> more effective on sentences where choice of words can decide the efficiency >> of translation. It's not about if "Watch out" could be " be careful" , it's >> about choosing words that can retain the urgency in "watch out". Sentiment >> information on original sentence can help in that. >> >> On Thu, Feb 27, 2020, 23:47 Scoop Gracie <scoopgra...@gmail.com> wrote: >> >>> 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 >>>> >>> _______________________________________________ >>> 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 >> > > > -- > *Khanna, Tanmai* > _______________________________________________ > Apertium-stuff mailing list > Apertium-stuff@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/apertium-stuff >
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