Fear finder

  • View
    903

  • Download
    1

Embed Size (px)

DESCRIPTION

reverse dictionary app

Text of Fear finder

  • 1.FearFinder Android NLP ProjectParinita & Ashley Gill Ling 575, CLMA, University of Washington,SeattleJune 2nd, 2010

2. Agenda Initial Proposal App Introduction NLP Techniques App Walk-through Possible Applications 2 3. Initial Proposal Drug database still an issue + plus drug suggestions , not a good advice to give, unless you are an MD For proof of concept we chose a simpler database On-board processing Still holds true loading the dictionaries can be improved. Attempts of accessing pre-existing DB not successful we are using text files3 4. App Introduction- FearFinderFearFinder A reverse dictionary that finds phobia names based on auser's input fear. Resources: A list of phobias and their meanings A subset of WordNet created by getting the synonyms of the words that occur in the meaning (a Preprocessing step) 4 5. NLP Techniques acarophobia- itching; insects that cause itching Synonyms insects-dirt ball cause-causal agency cause-get itching-itchiness insects-louse cause-causal agent cause-grounds itching-itching insects-worm cause-crusade cause-have itching-rub cause-do cause-induce itching-scabies cause-drive cause-lawsuit itching-scratch cause-effort cause-movement itching-spoil cause-stimulate cause-reason itching-urge cause-suit WordNet is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations.5 6. NLP Techniques Extracting Synonyms from WordNet Query for extracting Synonyms Wordnet30 SQLite DB" select synsetid, w2.lemma from The entire Wordnetsense left join word as w2 on 47.5 MB 47.5 MB w2.wordid=sense.wordid where sense.synsetid in"+ ANDROID_WN_DB " ( select sense.synsetid from Only words that we needword as w1"+ 284 KB 284 KB " left join sense on w1.wordid=sense.wordid"+ " where ANDROID_WN_TXT (w1.lemma='"+defword+"' or 176KB 176 KB +18 KBw1.lemma='"+stem+"') )"+ " and (w2.lemma'"+defword+"' or w2.lemma'"+stem+"') group by w2.lemma having count(*)=1;");6 7. App Walk Through7 8. App Walk Through8 9. App Walk Through9 10. App Walk Through10 11. App Walk Through11 12. App Walk Through12 13. Issues: It is limited by the words that are present in the List. Look up an external site if not found.. On-board Processing is not the best way. Too many condition checks hits performance. Using databases will definitely be better, but their size will still be limited. Read from text file and write into a database, instead of a dictionary ?13 14. Possible Applications The same concept can be applied to other domains all that needs to be changed is the resource list of words and meanings. Install list as requested ..?MythologicalDrugsCreaturesScientificRecipes Names ??14