5,710
edits
Changes
NLP
,no edit summary
== Opinion Mining ==
http://wiki.cse.cuhk.edu.hk/irwin.king/kb/opinionmining
SensiWordNet - a publicly available resource for opinion mining. *http://nmis.isti.cnr.it/sebastiani/Publications/LREC06.pdf == Natural Language Interface to a Video Data Model ==http://etd.lib.metu.edu.tr/upload/12606251/index.pdf http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=4061311&isnumber=4061295 == Data mining==http://en.wikipedia.org/wiki/Principal_components_analysis == NLP Language Research===== generation of conjectures ===* 'Automatic Conjecture Generation in the Digital Humanities' by Patrick Juola and Ashley Bernola ** http://twitter.com/conjecturator === resources ===* Wordnet* Framenet* OpenConceptNet http://conceptnet.media.mit.edu/* Cyc* http://www.cse.ohio-state.edu/~dbyron/788AU06/initial-handout.pdf === basic Content Analysis ===* http://www.williamlowe.net/software/ca-in-python.html=== Identity as a Variable ===* http://www.ucd.ie/euiteniba/pdf/Identity%20as%20a%20Variable.pdf === Closed Captioning ===* http://www.dcmp.org/captioningkey/* http://en.wikipedia.org/wiki/Closed_captioning* Caption it yourself http://www.dcmp.org/ciy/ === Teleprompter ===Language for performance[[Teleprompter]] === Wordnet similarity ===wordnet::similarity word vectors* coffee#n#1: nutmeg, preparation, coffee_tree, caffeine, pulverized, coffee, arabia, tea, shelf_life, packed, hot_water, drinking, topped, java, lemon_peel, ordered, cognac, irish_whiskey, beverage, perforated, cup, sweetened, infusion, espresso, cream, boiling, dehydrated, finely, whipped_cream, stimulating, bitter, cinnamon, alkaloid* cup#n#1: loaded, drunk, disposable, mustache, coffee, boxlike, tea, saucer, footed, drinking, eucharistic, collectively, tableware, bowl, cup, standardized, greece, rim, toast, missing, ancient_greek, drinking_vessel * rifle#n#1: automatic_rifle, loads, loading, rifle, action_mechanism, bore, barrel, butt_end, breech, cartridge, fired, sliding, firearm, shotgun, lever, armored, shoulder_holster, portable, lifted, semiautomatic, forward_motion, rifled* work#n#1: dry_rot, busywork, willing, machine_tool, technology, practical, barn, shiny, undertaking, productive, wages, inquiring, waiting, unit_of_time, preliminary, cleansing, substitute, incomplete, obliged, operations, a_great_deal, rubbing, washed, separately, ophelia, outstanding, interesting, waxing, stocks, attending, papers, labor, thoroughly, heavy_lifting, wasnt, municipal, succeed, missionary, hoped, further, mechanical, medical_care, barber, assigned, meager, budget, course_of_study, attempted, disadvantaged, boss, routine, damaging, close_to, done_for, grade, directed, systematically, recreational, duties, checked, shining, shoes, piece_of_work, polishing, sunday, soap, gawkers, leave_of_absence, improve, no_longer, cleaning, housewife, handling* neck: sternum, collarbone, hanging, immunity, human_being, cartilaginous, externally, cervix, aids, elderly, body_part, glandular, rings, flex, mastoid, clavicle, admired, occipital_bone, arteries, larynx, membranous, inhaled, aorta, fold, obliquely, artery, ductless, spine, on_fire, chin, graceful* rifle#n#1 <-> neck#n#1 = 0.0728235790450328 === Generative Video through NLP ===* making edits based on semantic content** sequence** cut points?** decisions based on character names? * aesthetic strategies for text** side-scrolling text** intertitles** subtitles** titles* aesthetic strategies for photos** Ken Burns effect** simulated motion *** motion blur [[Main_Page#Motion Estimation]]*** camera shake*** applying motion parameters extracted from real-life situation* aesthetic strategies for video** specifically... diverse collections of heterogenous clips and documentation** video collection strategies === similarity measures ===Calculating the similarity of two phrases, with the goal of finding more related matches. Example: "ocean at night" to "still life, objects around the studio" versus "cat tails at the ocean"* http://www.google.com/search?q=calculate+similarity+of+two+phrases&hl=en&start=20&sa=N* http://stackoverflow.com/questions/70560/how-do-i-compare-phrases-for-similarity* http://en.wikipedia.org/wiki/Document_classification* Phrase-based Document Similarity Based on an Index Graph Model http://www2007.org/papers/paper632.pdf* http://pami.uwaterloo.ca/pub/hammouda/hammouda_icdm02.pdf [[NLP]] Optical Character Recognition* tesseract-ocr [http://code.google.com/p/tesseract-ocr/]* ocropus [http://code.google.com/p/ocropus/] layout analysis, tesseract is a plugin [http://www.hpcwire.com/offthewire/University-of-Reading-Scientists-Study-Word-Evolution-40356217.html word evolution study at University of Reading] * Professor Mark Pagel [http://www.evolution.reading.ac.uk/ http://www.evolution.reading.ac.uk/]* ThamesBlue [[language associations]] Closed Captioning* http://en.wikipedia.org/wiki/Closed_captioning === Electronic Literature as Performance ===* http://www.drunkenboat.com/db10/05ele/elite.html