Hi Lance, thanks for your answer here. Is there a way to contact you (skype) for a small talk. That would help me a lot. Or can you recommend someone who is experienced?
I m not into computer linguistic in detail. In past, i wrote my own algorithms, compined them with stemming and levinstein but openNLP would offer a totally different approach - i guess. Rright now, i just need someone who pushes me into the right direction. Any help appreciated .... :) Dan -------- Original-Nachricht -------- > Datum: Mon, 25 Jun 2012 01:27:07 -0700 > Von: Lance Norskog <[email protected]> > An: [email protected] > Betreff: Re: Newby Question on German POS Tagging > The Chunking tool might help here. Chunking means finding noun and > verb phrases. This can help you find recurring phrases. Because German > is agglutinative, this is probably a very different problem than in > English. Are there any de-agglutinizer algorithms? > > On Sun, Jun 24, 2012 at 11:43 PM, daniel stieger <[email protected]> > wrote: > > Hi, > > > > thanks a lot for your answers. My goal is to identify adjectives and > nouns in association sentence. Eg. What do you associate with our brand? > Answer: nice mountains, the mountains are very nice .. etc. > > > > > > If appropriate, i would use the openNLP posTagger (it seams to be the > most elaborated java postagger) in order to identify nouns and adjectives. So > when i input the sentence "the", "mountains", "are", "nice" > > the output is correct - also when using single words: > > > >>> [DT, NNS, VBP, JJ] > >>> [DT] > >>> [NNS] > >>> [VBP] > >>> [JJ] > > > > > > Is the english model better than the german model? Do i have to build my > own model - or is the de-maxent appropriate? > > > > Generally - is openNLP a good choice for my task? > > > > Thanks again, > > Dan > > > > > > -------- Original-Nachricht -------- > >> Datum: Sat, 23 Jun 2012 16:53:36 -0700 > >> Von: Lance Norskog <[email protected]> > >> An: [email protected] > >> Betreff: Re: Newby Question on German POS Tagging > > > >> What would you like to find out about your data? Until we know that it > >> is difficult to recommend a technique. > >> > >> On Sat, Jun 23, 2012 at 4:15 AM, Thilo Goetz <[email protected]> wrote: > >> > On 22.06.2012 20:13, daniel stieger wrote: > >> >> > >> >> Hi List, > >> >> > >> >> i m looking for some suggestions and opinions for my task. The > >> situation > >> >> is this: > >> >> > >> >> In an online survey approx. 800 participants were asked a open text > >> >> question like "What do you associate with our brand?". Participants > can > >> then > >> >> enter 5 associations. Eg. > >> >> > >> >> - nature > >> >> - beautifull mountains > >> >> - relax > >> >> - family friendly > >> >> - very good service > >> >> > >> >> > >> >> Now i just want to run the openNLP Post tagger over all > associations. I > >> >> suppose that i can use one association just as one sentence. Instead > of > >> the > >> >> english model, i used the de-maxent.bin model and some german > answers. > >> But > >> >> the tags are somehow wrong. Eg. > >> >> > >> >> sonne -> KON > >> >> familie -> ART (it is a noun, definitely not an aricle) > >> >> > >> >> Am I on a wrong path? Should i handle my data differently? Or should > i > >> >> download an other model? Where can i get trainingdata ?? > >> >> > >> >> So many questions.. sorry.. but every hint appreciated, > >> >> > >> >> best, > >> >> Daniel > >> >> > >> >> > >> > > >> > I'm pretty sure the model was trained on complete sentences. The > >> > tagging takes context into account, and will not work properly > >> > without it. So just running it on a couple of words at a time > >> > will not work. > >> > > >> > If all your associations are NPs like your example, > >> > you can maybe fix things by always prefixing "I like the ". In > >> > German, maybe "Ich liebe ". > >> > > >> > HTH, > >> > Thilo > >> > > >> > > >> > >> > >> > >> -- > >> Lance Norskog > >> [email protected] > > > > -- > > NEU: FreePhone 3-fach-Flat mit kostenlosem Smartphone! > > Jetzt informieren: http://mobile.1und1.de/?ac=OM.PW.PW003K20328T7073a > > > > -- > Lance Norskog > [email protected] -- NEU: FreePhone 3-fach-Flat mit kostenlosem Smartphone! Jetzt informieren: http://mobile.1und1.de/?ac=OM.PW.PW003K20328T7073a
