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Treebank Statistics: UD_Italian-TWITTIRO: POS Tags: NOUN

There are 1790 NOUN lemmas (35%), 2050 NOUN types (31%) and 4455 NOUN tokens (15%). Out of 16 observed tags, the rank of NOUN is: 1 in number of lemmas, 1 in number of types and 2 in number of tokens.

The 10 most frequent NOUN lemmas: scuola, governo, anno, riforma, professore, cosa, retweet, docente, italiano, lavoro

The 10 most frequent NOUN types: governo, scuola, riforma, anni, RT, docenti, anno, lavoro, cosa, casa

The 10 most frequent ambiguous lemmas: scuola (NOUN 287, PROPN 1), cosa (NOUN 28, PRON 21), docente (NOUN 26, PROPN 1), italiano (ADJ 30, NOUN 26), ministro (NOUN 22, PROPN 3), casa (NOUN 20, PROPN 1), libro (NOUN 14, PROPN 1), ora (ADV 43, NOUN 15), cazzo (NOUN 13, INTJ 2), uomo (NOUN 11, PROPN 1)

The 10 most frequent ambiguous types: cosa (NOUN 20, PRON 13), italiani (NOUN 18, ADJ 7), parte (NOUN 13, VERB 2), politica (NOUN 12, ADJ 1), precari (NOUN 11, ADJ 1), cazzo (NOUN 8, INTJ 2), faccia (NOUN 9, VERB 3), legge (NOUN 6, VERB 2), punti (NOUN 9, VERB 1), uomo (NOUN 9, PROPN 1)

• cosa
  • NOUN 20: Mh , dovrei scrivere qualcosa in la bio ma non so cosa lol
  • PRON 13: 45 . cosa pensi di il governo Monti ? Possiamo non parlar ne !?
• italiani
  • NOUN 18: governo monti italiani tonti
  • ADJ 7: Mario Monti ha usato come metafora firewall e i giornalisti italiani annaspano .
• parte
  • NOUN 13: @user1 @user2 #labuonascuola ne fanno parte anche i 600 milioni di tagli ?
  • VERB 2: Il Governo Monti parte …. c’ è larga BANCA INTESA . http://t.co/x0u6nt7b
• politica
  • NOUN 12: Cicciolina : “ Quando facevo politica ero come Grillo “ . Circondata da cazzoni . [ rubagalline ]
  • ADJ 1: @user e meno male che la scuola serve per educare . Il Caovur sembra essere diventato una tribuna politica . #labuonascuola
• precari
  • NOUN 11: Super SPOT : “ 150mila precari saranno assunti “ ( cit. ) #scuola #labuonascuola
  • ADJ 1: #Salvini a #Renzi “ basta prof precari meridionali a il Nord “ … Lavora con #Razzi per mandar li tutti in Corea ? #labuonascuola è una cosa seria
• cazzo
  • NOUN 8: Vladimir Luxuria incontra Berlusconi . Chissà di che cazzo hanno parlato . [ @user ]
  • INTJ 2: RT @user : Persino a Paperopoli i politici erano onesti , cazzo .
• faccia
  • NOUN 9: @user già la faccia di la tua icon allude ad un pene invisibile #messaggisubliminali
  • VERB 3: Marco Rizzo vuole rifondare il Pci . Prima che lo faccia Mastella . [ @user ]
• legge
  • NOUN 6: In fondo il ministro Guidi ha solo dirottato una legge per amore . [ @user ]
  • VERB 2: Scilipoti legge i passi di la Bibbia contro gli omosessuali . Chissà Dio cosa gli ha offerto in cambio . [ @user ]
• punti
  • NOUN 9: Ma i punti de #labuonascuola sono riferiti anche a quella pubblica ? #perdire
  • VERB 1: Bersani : « Governo Monti punti a il cuore di gli italiani » . Il fegato se lo sono rovinati con il governo Berlusconi .
• uomo
  • NOUN 9: Mario Monti è un uomo di Stato : ma di quale Stato ?
  • PROPN 1: In il Governo Monti , Amato dr sottile e Letta uomo di similaun ? Mancano solo Andreotti e Forlani !

Morphology

The form / lemma ratio of NOUN is 1.145251 (the average of all parts of speech is 1.274961).

The 1st highest number of forms (6) was observed with the lemma “professore”: prof, prof., professor, professore, professori, proof.

The 2nd highest number of forms (5) was observed with the lemma “ragazzo”: ragazza, ragazzi, ragazzini, ragazzino, ragazzo.

The 3rd highest number of forms (4) was observed with the lemma “governo”: gorverno, gov, governi, governo.

NOUN occurs with 2 features: Gender (4195; 94% instances), Number (4047; 91% instances)

NOUN occurs with 4 feature-value pairs: Gender=Fem, Gender=Masc, Number=Plur, Number=Sing

NOUN occurs with 9 feature combinations. The most frequent feature combination is Gender=Masc|Number=Sing (1508 tokens). Examples: governo, lavoro, anno, mondo, merito, ministro, posto, paese, libro, tempo

Relations

NOUN nodes are attached to their parents using 35 different relations: nmod (920; 21% instances), obl (872; 20% instances), obj (743; 17% instances), nsubj (575; 13% instances), parataxis (360; 8% instances), root (305; 7% instances), conj (212; 5% instances), nsubj:pass (68; 2% instances), appos (58; 1% instances), compound (53; 1% instances), dep (34; 1% instances), ccomp (32; 1% instances), vocative (29; 1% instances), xcomp (26; 1% instances), discourse (24; 1% instances), dislocated (24; 1% instances), obl:agent (22; 0% instances), parataxis:appos (17; 0% instances), advcl (15; 0% instances), flat (12; 0% instances), parataxis:obj (8; 0% instances), case (7; 0% instances), flat:name (6; 0% instances), acl:relcl (5; 0% instances), amod (4; 0% instances), iobj (4; 0% instances), list (4; 0% instances), vocative:mention (4; 0% instances), parataxis:hashtag (3; 0% instances), acl (2; 0% instances), flat:foreign (2; 0% instances), parataxis:nsubj (2; 0% instances), csubj (1; 0% instances), orphan (1; 0% instances), parataxis:insert (1; 0% instances)

Parents of NOUN nodes belong to 14 different parts of speech: VERB (2414; 54% instances), NOUN (1322; 30% instances), (305; 7% instances), ADJ (133; 3% instances), PROPN (94; 2% instances), PRON (61; 1% instances), SYM (56; 1% instances), ADV (33; 1% instances), NUM (15; 0% instances), INTJ (10; 0% instances), X (7; 0% instances), ADP (3; 0% instances), AUX (1; 0% instances), DET (1; 0% instances)

301 (7%) NOUN nodes are leaves.

1053 (24%) NOUN nodes have one child.

1472 (33%) NOUN nodes have two children.

1629 (37%) NOUN nodes have three or more children.

The highest child degree of a NOUN node is 14.

Children of NOUN nodes are attached using 45 different relations: det (2681; 25% instances), case (1858; 17% instances), nmod (1511; 14% instances), punct (1089; 10% instances), amod (828; 8% instances), parataxis (313; 3% instances), cop (255; 2% instances), advmod (214; 2% instances), conj (211; 2% instances), nummod (185; 2% instances), cc (173; 2% instances), nsubj (167; 2% instances), parataxis:hashtag (159; 1% instances), vocative:mention (141; 1% instances), acl:relcl (104; 1% instances), acl (102; 1% instances), det:poss (98; 1% instances), dep (90; 1% instances), obl (84; 1% instances), compound (59; 1% instances), discourse (59; 1% instances), mark (51; 0% instances), appos (44; 0% instances), det:predet (29; 0% instances), flat (25; 0% instances), advcl (23; 0% instances), parataxis:appos (18; 0% instances), flat:name (17; 0% instances), csubj (12; 0% instances), obj (12; 0% instances), aux (11; 0% instances), vocative (11; 0% instances), fixed (10; 0% instances), iobj (9; 0% instances), discourse:emo (8; 0% instances), ccomp (7; 0% instances), list (3; 0% instances), orphan (3; 0% instances), parataxis:insert (3; 0% instances), parataxis:obj (3; 0% instances), dislocated (2; 0% instances), xcomp (2; 0% instances), expl (1; 0% instances), parataxis:discourse (1; 0% instances), parataxis:nsubj (1; 0% instances)

Children of NOUN nodes belong to 16 different parts of speech: DET (2811; 26% instances), ADP (1853; 17% instances), NOUN (1322; 12% instances), PUNCT (1089; 10% instances), ADJ (879; 8% instances), PROPN (650; 6% instances), SYM (598; 6% instances), VERB (384; 4% instances), AUX (266; 2% instances), ADV (250; 2% instances), NUM (197; 2% instances), CCONJ (182; 2% instances), PRON (95; 1% instances), SCONJ (46; 0% instances), INTJ (42; 0% instances), X (23; 0% instances)

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