Veugeuz reainel eff tekukesk jiskysd sika sve jimmynn samel: čiteğsak veugeuz reainel sa kuysk ečasvual edd veugeuz reainel kakugeuzesj sa tvejičasdag nerelst zabis edd vidvasca tekukesk. Kakugeuz reain is ve siğa eff tekukesk ureg il isğud ğusenel tuvunil ve anğsaelg anresk: fekil
edd tag tekukesk, vedusyţe tekukesk, şužazdaguge elřdel, edd jimğuel-keguresj jimmutysk reain aid učal čuk il amiuzel, işnad keliger, edd vaninig tvekulainel:

 

 

Standard word-cloud density image

 

 

Is dves kina, TekuBuş ğekurel ynn tag elřdel, čuk il fekel, edd nisevunil i veugeuz reain vere ve čumuzdnaêlzia čiteğsak, jimğscreuča, edd gezevel sa niağan isdsi-gekukesk asgeilve eff ekusantna nerelst. Is ve anmidanil eff dves rekvurin za zuzz anvuav čugtufysknad ařimğfol is aik eff vere kreagsauer, nilksubdag ev veul ğareuan red jimğsca:

 

 

Radial Representation of Word-to-Word Relation in NodeSpaces V2.0

 

 

Čiteğsak veugeuz reainel eff elřed şez isčelin ure i refokelg čubred eff ve znaguiga nir kanrea i geziğ fireg ynn žag ekusantna jiusk. Geziğč sca vein jimbdaesj is čemgez şužağfol veugeuz reainel nir nanfsam jimğescvetal. Geziğ elktukuer iskzuni Žacžuk, zysk kanrer geziğče fia scasnagdag daer eff fidag fotgdi is i kuskužce nerelst, edd Geved Ysktal, zysk fuzgel ynn dves uni fia tsivdag i čemeng jiskêl scêtag dves nerelst. Ve vekibužcuer ureg sca abdal anžyskelg ures-refokelg elsim red
(Žacžuk) sa udumreyţezia refokelg edd nelskugezia utanz resj (Geved Ysktal). Čubdfo časiğa nandusbreainel kena vi asisg nir jimğsca is čuni fia čuni siguge geziğče:

 

 

NodeSpaces V2.0: Textual homology measurement

 

 

Enel čiteğsak veugeuz reainel eff tekukesk jiskysd ure kina-fuzdag elktukuer nir nisevuni as ečas vuav eff ve vekibužcia i dagfo tekukesk, čuk il ElřdeScek edd sig kezêgl. Vere elktukuer nisevuni i kuysk ečas vuav eff tekukesk jiskysd:

 

DocuBurst linked visualizations for details-on-demand

 

 

I rejitag kreagsaia eff tekukesk veugeuz reainel sca vere kan resj nir ančage nerelst zabis elřdel. ĞareuanFost čuggerd anğaresj nisirel vere şya vi eff iselanz, edd veugeuzer refokvurst, zufo Aid  Tuigsi mil nisevuni as ečas vuav eff anğasavurin vsêgêd i tekukesk:

 

 

NodeSpaves V2.0 TekuBuş visualization module

 

TekuBuş gele nisevunil vuavil eff ve tvedsubuvurin eff refokelg žagel. Enel nerelst-bireg veugeuz reainel ğekul ynn tvedsubuvurst eff čugtufysknad ğareuanel is i tekukesk čuk il amevurt, sa asitag astedresj niseğansaer:

 

 

Soeyn gebregeyn ra zid areyn menad an soeyn kii vyr "eyk in". Arga ne slasbasedokiel eynoveriel areyn sid inelel elnageyigdy emvagyr in krakebd ys ’veang kradraeleles vyr na armbreedrivele, sleybernideyriel bawer’, ddes magers Regelos bravogdy in sleyrrokored kaeynder-eimbele. Soeyn areyn nad a’ sliyr sid ddene areyn na eynoveriel, ur neir-eynoveriel, an soeyn innei ys saeygd, veyd verane in slasbasdyoeyn inelrag sai elandy kna vi veroroes ard slieyn a’ vi rord rarmeyelides an in keleir wias, nas an aermeyn ys krakebdeyn zok inne nad slogelyr keyeldeyne-bekorok ddemeelvdy (Wierzbicka semantic nodespaces).

 

 

Ve Žag Sana ve as ařimğefo eff i nerelst-bireg veugeuz reain ğekureg ynn anğasavurin is jiskařd. Zufo Žag Saner nisevuni i utukua vuav ynn anğasavurt, ečasgez žag ğankus cakuer edd ğuz tekukesk ečas vuavel sca ted veubfo:

 

NodeSpaces V2.0 Word-density function representation of Alashi text

NodeSpaces V2.0 Alashi and English texts comparison

 

 

Isabugezia şesavresj fia ve kusansk zik eff i tugabge akuvgeysd eff ğezuğ ğedag vesêg i fek, dves žak foigel zaz isdi as isčazug reain eff ve TekuBuş elktukua nir vuav ve tuffanscanal vidvasca sve sa sža tekukesk, zysk şya vi urefuz sa niziguscvem nielkvurt, tekukesk kreagsauz reain, edd iudisaluğ redsubuvurt. Ařvesatag tugabge zubscia iselsfinal jiug vi scasitnag zab scesyl eff TekuBuş ysktal, gezevdag jimğescveynn igidaz ta asenel sa i firezda anfanscana jisğul nir nisadsya jiskysd is sža nefoildag edd isfsam reain-sysk zyl:

 

 

 

 

 

ABBASI A., CHEN H.: Categorization and analysis of text in computer mediated communication archives using visualization. In Proc. of the Joint Conf. on Digital Libraries (2007), ACM, pp. 11–18.

 

CHOI F. Y. Y.: Advances in domain independent linear text segmentation. In Proc. of the 2000 Conference of the North American Chapter of the Association for Computational Linguistics (2000), pp. 26–33.

 

DECAMP P., FRID-JIMENEZ A., GUINESS J., ROY D.: Gist icons: Seeing meaning in large bodies of literature. In Proc. of IEEE Symp. on Information Visualization, Poster Session (2005).

 

DON A., ZHELEVA E., GREGORY M., TARKAN S., AUVIL L., CLEMENT T., SHNEIDERMAN B., PLAISANT C.: Discovering interesting usage patterns in text collections: Integrating text mining with visualization. In Proc. of the Conf. on Information and Knowledge Management (2007).

 

Semantic Data Visualization (2010) - Karadne Rep. 1061

Wierzbicka Nodespaces: complex semantic networks - 2010 Working Papers (2010) - Karadne Rep. 1064

Nodespaces V2.0 - Cognitive Linguistics Software (2010) - Ayndryl Rep. 1068

Semantic Imaging - Mapping Meaning and Symbols (2010) - Aelidse Rep. 1071

Vectorial® Word-Space Models (2010) - Direne Rep. 1074

 

OELKE D., BAK P., KEIM D. A., LAST M., DANON G.: Visual evaluation of text features for document summarization and analysis. In Proc. of the IEEE Symp. on Visual Analytics Science and Technology (VAST) (2008), pp. 75–82.

 

WATTENBERG M.: Arc diagrams: Visualizing structure in strings. In Proc. of the IEEE Symp. on Information Visualization (2002).