(TokenFeatureGenerator) Loading features...
[orth, base, ctag, synonym, hypernym-1, hypernym-2, hypernym-3, top4hyper-1, top4hyper-2, top4hyper-3, class, case, number, gender, pattern, prefix-1, prefix-2, prefix-3, prefix-4, suffix-1, suffix-2, suffix-3, suffix-4, all_alphanumeric, all_digits, all_letters, all_upper, has_upper_case, has_lower_case, has_symbol, has_digit, starts_with_upper_case, starts_with_lower_case, starts_with_symbol, starts_with_digit, is_number, no_alphanumeric, no_letters, struct, length, dict_person_first_nam, dict_person_last_nam, dict_country_nam, dict_city_nam, dict_road_nam, dict_person_prefix, dict_person_noun, dict_person_suffix, dict_road_prefix, dict_country_prefix, dict_trigger_int_bloc, dict_trigger_ext_bloc, dict_trigger_int_country, dict_trigger_ext_country, dict_trigger_int_district, dict_trigger_ext_district, dict_trigger_int_geogName, dict_trigger_ext_geogName, dict_trigger_int_orgName, dict_trigger_ext_orgName, dict_trigger_int_persName, dict_trigger_ext_persName, dict_trigger_int_region, dict_trigger_ext_region, dict_trigger_int_settlement, dict_trigger_ext_settlement, agr1, parenthesis, quotation, nospace]
Annotations to evaluate: ^nam$
/nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.ini
/nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.ini
-> Setting up chunker: chunker_c1
(TokenFeatureGenerator) Loading features...
[orth, base, ctag, synonym, hypernym-1, hypernym-2, hypernym-3, top4hyper-1, top4hyper-2, top4hyper-3, class, case, number, gender, pattern, prefix-1, prefix-2, prefix-3, prefix-4, suffix-1, suffix-2, suffix-3, suffix-4, all_alphanumeric, all_digits, all_letters, all_upper, has_upper_case, has_lower_case, has_symbol, has_digit, starts_with_upper_case, starts_with_lower_case, starts_with_symbol, starts_with_digit, is_number, no_alphanumeric, no_letters, struct, length, dict_person_first_nam, dict_person_last_nam, dict_country_nam, dict_city_nam, dict_road_nam, dict_person_prefix, dict_person_noun, dict_person_suffix, dict_road_prefix, dict_country_prefix, dict_trigger_int_bloc, dict_trigger_ext_bloc, dict_trigger_int_country, dict_trigger_ext_country, dict_trigger_int_district, dict_trigger_ext_district, dict_trigger_int_geogName, dict_trigger_ext_geogName, dict_trigger_int_orgName, dict_trigger_ext_orgName, dict_trigger_int_persName, dict_trigger_ext_persName, dict_trigger_int_region, dict_trigger_ext_region, dict_trigger_int_settlement, dict_trigger_ext_settlement, agr1, parenthesis, quotation, nospace]
--> CRFPP Chunker deserialize from /home/czuk/nlp/eclipse/workspace_liner2/liner2_master/../models-workdir/liner2.5/liner25_model_ner_kpwr12/data/bins/model_crfpp_kpwr_names_train_tune_jrip.bin
(TemplateFactory) parsing template: /home/czuk/nlp/eclipse/workspace_liner2/liner2_master/../models-workdir/liner2.5/liner25_model_ner_kpwr12/ini/template-jrip.txt
(TemplateFactory) Adding feature:orth:-2:-1:0:1:2
(TemplateFactory) feature:orth:-2:-1:0:1:2
(TemplateFactory) Adding feature:base:-2:-1:0:1:2
(TemplateFactory) feature:base:-2:-1:0:1:2
(TemplateFactory) Adding feature:synonym:-2:-1:0:1:2
(TemplateFactory) feature:synonym:-2:-1:0:1:2
(TemplateFactory) Adding feature:hypernym-1:-2:-1:0:1:2
(TemplateFactory) feature:hypernym-1:-2:-1:0:1:2
(TemplateFactory) Adding feature:hypernym-2:-2:-1:0:1:2
(TemplateFactory) feature:hypernym-2:-2:-1:0:1:2
(TemplateFactory) Adding feature:hypernym-3:-2:-1:0:1:2
(TemplateFactory) feature:hypernym-3:-2:-1:0:1:2
(TemplateFactory) Adding feature:class:-1:0:1
(TemplateFactory) feature:class:-1:0:1
(TemplateFactory) Adding feature:case:0
(TemplateFactory) feature:case:0
(TemplateFactory) Adding feature:gender:-2:-1:0:1:2
(TemplateFactory) feature:gender:-2:-1:0:1:2
(TemplateFactory) Adding feature:pattern:0
(TemplateFactory) feature:pattern:0
(TemplateFactory) Adding feature:prefix-1:-2:-1:0:1:2
(TemplateFactory) feature:prefix-1:-2:-1:0:1:2
(TemplateFactory) Adding feature:prefix-3:-2:-1:0:1:2
(TemplateFactory) feature:prefix-3:-2:-1:0:1:2
(TemplateFactory) Adding feature:prefix-4:-2:-1:0:1:2
(TemplateFactory) feature:prefix-4:-2:-1:0:1:2
(TemplateFactory) Adding feature:suffix-1:-2:-1:0:1:2
(TemplateFactory) feature:suffix-1:-2:-1:0:1:2
(TemplateFactory) Adding feature:suffix-2:-2:-1:0:1:2
(TemplateFactory) feature:suffix-2:-2:-1:0:1:2
(TemplateFactory) Adding feature:suffix-3:-2:-1:0:1:2
(TemplateFactory) feature:suffix-3:-2:-1:0:1:2
(TemplateFactory) Adding feature:suffix-4:-2:-1:0:1:2
(TemplateFactory) feature:suffix-4:-2:-1:0:1:2
(TemplateFactory) Adding feature:starts_with_upper_case:-2:-1:0:1:2
(TemplateFactory) feature:starts_with_upper_case:-2:-1:0:1:2
(TemplateFactory) Adding feature:starts_with_lower_case:-2:-1:0:1:2
(TemplateFactory) feature:starts_with_lower_case:-2:-1:0:1:2
(TemplateFactory) Adding feature:starts_with_symbol:-2:-1:0:1:2
(TemplateFactory) feature:starts_with_symbol:-2:-1:0:1:2
(TemplateFactory) Adding feature:starts_with_digit:-2:-1:0:1:2
(TemplateFactory) feature:starts_with_digit:-2:-1:0:1:2
(TemplateFactory) Adding feature:has_upper_case:-2:-1:0:1:2
(TemplateFactory) feature:has_upper_case:-2:-1:0:1:2
(TemplateFactory) Adding feature:has_lower_case:-2:-1:0:1:2
(TemplateFactory) feature:has_lower_case:-2:-1:0:1:2
(TemplateFactory) Adding feature:has_symbol:-2:-1:0:1:2
(TemplateFactory) feature:has_symbol:-2:-1:0:1:2
(TemplateFactory) Adding feature:has_digit:-2:-1:0:1:2
(TemplateFactory) feature:has_digit:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_person_first_nam:-2:-1:0:1:2
(TemplateFactory) feature:dict_person_first_nam:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_person_last_nam:-2:-1:0:1:2
(TemplateFactory) feature:dict_person_last_nam:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_country_nam:-2:-1:0:1:2
(TemplateFactory) feature:dict_country_nam:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_city_nam:-2:-1:0:1:2
(TemplateFactory) feature:dict_city_nam:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_road_nam:-2:-1:0:1:2
(TemplateFactory) feature:dict_road_nam:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_person_prefix:-2:-1:0:1:2
(TemplateFactory) feature:dict_person_prefix:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_person_noun:-2:-1:0:1:2
(TemplateFactory) feature:dict_person_noun:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_person_suffix:-2:-1:0:1:2
(TemplateFactory) feature:dict_person_suffix:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_road_prefix:-2:-1:0:1:2
(TemplateFactory) feature:dict_road_prefix:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_country_prefix:-2:-1:0:1:2
(TemplateFactory) feature:dict_country_prefix:-2:-1:0:1:2
(TemplateFactory) Adding feature:base:-4/dict_person_first_nam:-3/dict_person_last_nam:-2/base:-1/dict_person_last_nam:0
(TemplateFactory) feature:base:-4/dict_person_first_nam:-3/dict_person_last_nam:-2/base:-1/dict_person_last_nam:0
(TemplateFactory) Adding feature:dict_trigger_int_bloc:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_int_bloc:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_ext_bloc:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_ext_bloc:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_int_country:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_int_country:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_ext_country:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_ext_country:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_int_district:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_int_district:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_ext_district:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_ext_district:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_int_geogName:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_int_geogName:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_ext_geogName:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_ext_geogName:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_int_orgName:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_int_orgName:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_ext_orgName:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_ext_orgName:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_int_persName:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_int_persName:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_ext_persName:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_ext_persName:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_int_region:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_int_region:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_ext_region:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_ext_region:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_int_settlement:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_int_settlement:-2:-1:0:1:2
(TemplateFactory) Adding feature:dict_trigger_ext_settlement:-2:-1:0:1:2
(TemplateFactory) feature:dict_trigger_ext_settlement:-2:-1:0:1:2
(TemplateFactory) Adding feature:agr1:-2:-1:0:1:2
(TemplateFactory) feature:agr1:-2:-1:0:1:2
(TemplateFactory) Adding feature:nospace:-2:-1:0:1:2
(TemplateFactory) feature:nospace:-2:-1:0:1:2
(TemplateFactory) Adding feature:parenthesis:-2:-1:0:1:2
(TemplateFactory) feature:parenthesis:-2:-1:0:1:2
(TemplateFactory) Adding feature:quotation:-2:-1:0:1:2
(TemplateFactory) feature:quotation:-2:-1:0:1:2
(TemplateFactory) Adding feature:length:-2:-1:0:1:2
(TemplateFactory) feature:length:-2:-1:0:1:2
(TemplateFactory) Adding feature:has_upper_case:0/agr1:-1/pattern:2/has_upper_case:-1/case:0/number:-1
(TemplateFactory) feature:has_upper_case:0/agr1:-1/pattern:2/has_upper_case:-1/case:0/number:-1
(TemplateFactory) Adding feature:has_upper_case:0/all_digits:-1/all_upper:-2/length:1/nospace:1
(TemplateFactory) feature:has_upper_case:0/all_digits:-1/all_upper:-2/length:1/nospace:1
(TemplateFactory) Adding feature:has_upper_case:0/all_upper:-1/pattern:-1/ctag:0
(TemplateFactory) feature:has_upper_case:0/all_upper:-1/pattern:-1/ctag:0
(TemplateFactory) Adding feature:has_upper_case:0/all_upper:-1/pattern:-1/has_upper_case:1
(TemplateFactory) feature:has_upper_case:0/all_upper:-1/pattern:-1/has_upper_case:1
(TemplateFactory) Adding feature:has_upper_case:0/all_upper:-1/pattern:-1/pattern:-2/agr1:1/pattern:0
(TemplateFactory) feature:has_upper_case:0/all_upper:-1/pattern:-1/pattern:-2/agr1:1/pattern:0
(TemplateFactory) Adding feature:has_upper_case:0/all_upper:-1/pattern:-1/pattern:-2/class:0
(TemplateFactory) feature:has_upper_case:0/all_upper:-1/pattern:-1/pattern:-2/class:0
(TemplateFactory) Adding feature:has_upper_case:0/all_upper:-1/starts_with_digit:-2/starts_with_lower_case:1/case:0
(TemplateFactory) feature:has_upper_case:0/all_upper:-1/starts_with_digit:-2/starts_with_lower_case:1/case:0
(TemplateFactory) Adding feature:has_upper_case:0/ctag:0/has_upper_case:1
(TemplateFactory) feature:has_upper_case:0/ctag:0/has_upper_case:1
(TemplateFactory) Adding feature:has_upper_case:0/ctag:0/parenthesis:0/class:2/agr1:2
(TemplateFactory) feature:has_upper_case:0/ctag:0/parenthesis:0/class:2/agr1:2
(TemplateFactory) Adding feature:has_upper_case:0/ctag:0/suffix-1:0
(TemplateFactory) feature:has_upper_case:0/ctag:0/suffix-1:0
(TemplateFactory) Adding feature:has_upper_case:0/dict_person_first_nam:-1/all_upper:-2/all_alphanumeric:2/starts_with_lower_case:2/all_alphanumeric:1
(TemplateFactory) feature:has_upper_case:0/dict_person_first_nam:-1/all_upper:-2/all_alphanumeric:2/starts_with_lower_case:2/all_alphanumeric:1
(TemplateFactory) Adding feature:has_upper_case:0/dict_person_first_nam:-1/has_digit:-2/has_lower_case:0/nospace:-1/nospace:-2/ctag:-1
(TemplateFactory) feature:has_upper_case:0/dict_person_first_nam:-1/has_digit:-2/has_lower_case:0/nospace:-1/nospace:-2/ctag:-1
(TemplateFactory) Adding feature:has_upper_case:0/dict_person_first_nam:-1/has_lower_case:-1/all_alphanumeric:2/class:0
(TemplateFactory) feature:has_upper_case:0/dict_person_first_nam:-1/has_lower_case:-1/all_alphanumeric:2/class:0
(TemplateFactory) Adding feature:has_upper_case:0/dict_person_first_nam:-1/has_lower_case:2/all_letters:-1/ctag:0/all_alphanumeric:1
(TemplateFactory) feature:has_upper_case:0/dict_person_first_nam:-1/has_lower_case:2/all_letters:-1/ctag:0/all_alphanumeric:1
(TemplateFactory) Adding feature:has_upper_case:0/dict_person_first_nam:-1/has_lower_case:-2/length:1
(TemplateFactory) feature:has_upper_case:0/dict_person_first_nam:-1/has_lower_case:-2/length:1
(TemplateFactory) Adding feature:has_upper_case:0/dict_person_first_nam:-1/has_lower_case:-2/starts_with_lower_case:1/orth:-1
(TemplateFactory) feature:has_upper_case:0/dict_person_first_nam:-1/has_lower_case:-2/starts_with_lower_case:1/orth:-1
(TemplateFactory) Adding feature:has_upper_case:0/dict_person_first_nam:-1/pattern:-1/class:0
(TemplateFactory) feature:has_upper_case:0/dict_person_first_nam:-1/pattern:-1/class:0
(TemplateFactory) Adding feature:has_upper_case:0/dict_person_first_nam:-1/pattern:1/parenthesis:0
(TemplateFactory) feature:has_upper_case:0/dict_person_first_nam:-1/pattern:1/parenthesis:0
(TemplateFactory) Adding feature:has_upper_case:0/starts_with_lower_case:1/case:0/orth:2
(TemplateFactory) feature:has_upper_case:0/starts_with_lower_case:1/case:0/orth:2
(TemplateFactory) Adding feature:has_upper_case:0/starts_with_lower_case:1/case:0/pattern:1/agr1:1/gender:0
(TemplateFactory) feature:has_upper_case:0/starts_with_lower_case:1/case:0/pattern:1/agr1:1/gender:0
(TemplateFactory) Adding feature:has_upper_case:-1/has_upper_case:0/nospace:-2/pattern:0
(TemplateFactory) feature:has_upper_case:-1/has_upper_case:0/nospace:-2/pattern:0
(TemplateFactory) Adding feature:has_upper_case:-1/starts_with_lower_case:1/all_letters:1/orth:0/starts_with_lower_case:-2
(TemplateFactory) feature:has_upper_case:-1/starts_with_lower_case:1/all_letters:1/orth:0/starts_with_lower_case:-2
(TemplateFactory) Adding feature:has_upper_case:-1/starts_with_lower_case:1/orth:0/nospace:-1
(TemplateFactory) feature:has_upper_case:-1/starts_with_lower_case:1/orth:0/nospace:-1
(TemplateFactory) Adding feature:has_upper_case:-1/starts_with_lower_case:1/parenthesis:-2/class:-2/orth:-1
(TemplateFactory) feature:has_upper_case:-1/starts_with_lower_case:1/parenthesis:-2/class:-2/orth:-1
(TemplateFactory) Adding feature:has_upper_case:-2/has_upper_case:0/orth:-1/nospace:-2
(TemplateFactory) feature:has_upper_case:-2/has_upper_case:0/orth:-1/nospace:-2
(TemplateFactory) Adding feature:has_upper_case:-2/starts_with_upper_case:0/orth:-1
(TemplateFactory) feature:has_upper_case:-2/starts_with_upper_case:0/orth:-1
(TemplateFactory) Adding feature:starts_with_lower_case:-1/has_upper_case:0/case:-1/case:2
(TemplateFactory) feature:starts_with_lower_case:-1/has_upper_case:0/case:-1/case:2
(TemplateFactory) Adding feature:starts_with_lower_case:-1/has_upper_case:0/pattern:-1/ctag:-1/pattern:0
(TemplateFactory) feature:starts_with_lower_case:-1/has_upper_case:0/pattern:-1/ctag:-1/pattern:0
(TemplateFactory) Adding feature:starts_with_lower_case:1/starts_with_lower_case:-1/all_letters:-1/parenthesis:-2/pattern:1/length:0
(TemplateFactory) feature:starts_with_lower_case:1/starts_with_lower_case:-1/all_letters:-1/parenthesis:-2/pattern:1/length:0
(TemplateFactory) Adding feature:starts_with_lower_case:-1/starts_with_upper_case:0/class:-1/agr1:0/gender:-1
(TemplateFactory) feature:starts_with_lower_case:-1/starts_with_upper_case:0/class:-1/agr1:0/gender:-1
(TemplateFactory) Adding feature:starts_with_lower_case:1/starts_with_upper_case:-1/class:-2/no_letters:0/length:0
(TemplateFactory) feature:starts_with_lower_case:1/starts_with_upper_case:-1/class:-2/no_letters:0/length:0
(TemplateFactory) Adding feature:starts_with_lower_case:1/starts_with_upper_case:-1/parenthesis:-2/agr1:-2/ctag:-2
(TemplateFactory) feature:starts_with_lower_case:1/starts_with_upper_case:-1/parenthesis:-2/agr1:-2/ctag:-2
(TemplateFactory) Adding feature:starts_with_lower_case:1/starts_with_upper_case:-1/parenthesis:-2/case:-1/ctag:0
(TemplateFactory) feature:starts_with_lower_case:1/starts_with_upper_case:-1/parenthesis:-2/case:-1/ctag:0
(TemplateFactory) Adding feature:starts_with_lower_case:1/starts_with_upper_case:-2/agr1:-2/ctag:-2/struct:-2
(TemplateFactory) feature:starts_with_lower_case:1/starts_with_upper_case:-2/agr1:-2/ctag:-2/struct:-2
(TemplateFactory) Adding feature:starts_with_lower_case:1/starts_with_upper_case:-2/agr1:-2/quotation:-1
(TemplateFactory) feature:starts_with_lower_case:1/starts_with_upper_case:-2/agr1:-2/quotation:-1
(TemplateFactory) Adding feature:starts_with_lower_case:1/starts_with_upper_case:-2/pattern:0/length:-1
(TemplateFactory) feature:starts_with_lower_case:1/starts_with_upper_case:-2/pattern:0/length:-1
(TemplateFactory) Adding feature:starts_with_lower_case:1/starts_with_upper_case:-2/starts_with_upper_case:0/length:-1/ctag:-1
(TemplateFactory) feature:starts_with_lower_case:1/starts_with_upper_case:-2/starts_with_upper_case:0/length:-1/ctag:-1
(TemplateFactory) Adding feature:starts_with_upper_case:0/agr1:-1/gender:0/number:0
(TemplateFactory) feature:starts_with_upper_case:0/agr1:-1/gender:0/number:0
(TemplateFactory) Adding feature:starts_with_upper_case:0/agr1:-1/orth:-1/class:-2
(TemplateFactory) feature:starts_with_upper_case:0/agr1:-1/orth:-1/class:-2
(TemplateFactory) Adding feature:starts_with_upper_case:0/class:0/gender:0/prefix-1:0/all_alphanumeric:1
(TemplateFactory) feature:starts_with_upper_case:0/class:0/gender:0/prefix-1:0/all_alphanumeric:1
(TemplateFactory) Adding feature:starts_with_upper_case:0/class:0/starts_with_lower_case:1/struct:2/agr1:1
(TemplateFactory) feature:starts_with_upper_case:0/class:0/starts_with_lower_case:1/struct:2/agr1:1
(TemplateFactory) Adding feature:starts_with_upper_case:0/ctag:0/parenthesis:0/class:1
(TemplateFactory) feature:starts_with_upper_case:0/ctag:0/parenthesis:0/class:1
(TemplateFactory) Adding feature:starts_with_upper_case:0/dict_person_first_nam:-1/class:0/parenthesis:2/ctag:0
(TemplateFactory) feature:starts_with_upper_case:0/dict_person_first_nam:-1/class:0/parenthesis:2/ctag:0
(TemplateFactory) Adding feature:starts_with_upper_case:0/dict_person_first_nam:-1/has_lower_case:2/has_digit:-2/gender:1
(TemplateFactory) feature:starts_with_upper_case:0/dict_person_first_nam:-1/has_lower_case:2/has_digit:-2/gender:1
(TemplateFactory) Adding feature:starts_with_upper_case:0/starts_with_lower_case:1/case:0/suffix-2:1
(TemplateFactory) feature:starts_with_upper_case:0/starts_with_lower_case:1/case:0/suffix-2:1
(TemplateFactory) Adding feature:starts_with_upper_case:0/starts_with_lower_case:1/pattern:1/length:2/nospace:1/case:0/ctag:2
(TemplateFactory) feature:starts_with_upper_case:0/starts_with_lower_case:1/pattern:1/length:2/nospace:1/case:0/ctag:2
(TemplateFactory) Adding feature:starts_with_upper_case:-1/starts_with_lower_case:1/all_digits:-2/has_upper_case:1/orth:0
(TemplateFactory) feature:starts_with_upper_case:-1/starts_with_lower_case:1/all_digits:-2/has_upper_case:1/orth:0
(TemplateFactory) Adding feature:starts_with_upper_case:-1/starts_with_lower_case:1/starts_with_upper_case:0/nospace:2/gender:-1
(TemplateFactory) feature:starts_with_upper_case:-1/starts_with_lower_case:1/starts_with_upper_case:0/nospace:2/gender:-1
(TemplateFactory) Adding feature:starts_with_upper_case:-1/starts_with_upper_case:0/all_upper:-2/agr1:1/has_lower_case:2
(TemplateFactory) feature:starts_with_upper_case:-1/starts_with_upper_case:0/all_upper:-2/agr1:1/has_lower_case:2
(TemplateFactory) Adding feature:starts_with_upper_case:-1/starts_with_upper_case:0/all_upper:-2/nospace:0
(TemplateFactory) feature:starts_with_upper_case:-1/starts_with_upper_case:0/all_upper:-2/nospace:0
(TemplateFactory) Adding feature:starts_with_upper_case:-1/starts_with_upper_case:0/pattern:2/suffix-1:-1
(TemplateFactory) feature:starts_with_upper_case:-1/starts_with_upper_case:0/pattern:2/suffix-1:-1
--> CRFPP Chunker deserialize done 
-> Setting up chunker: chunker_rule_title
-> Setting up chunker: chunker_pipe
-> Setting up chunker: chunker_cp
--> Chunk propagation
(ChunkerEvaluator) Sentence #1 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent1

Text  : Listy .
Tokens: 1____ 2

Chunks:

(ChunkerEvaluator) Sentence #2 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent2

Text  : Moje spostrzeżenia z peronu pierwszego
Tokens: 1___ 2____________ 3 4_____ 5_________

Chunks:

(ChunkerEvaluator) Sentence #3 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent3

Text  : Chciał by m się podzielić swoimi spostrzeżeniami z nowo otwartego peronu pierwszego na krakowskim Dworcu Głównym .
Tokens: 1_____ 2_ 3 4__ 5________ 6_____ 7______________ 8 9___ 10_______ 11____ 12________ 13 14________ 15____ 16_____ 17

Chunks:
  TruePositive nam [15,16] = Dworcu Głównym

(ChunkerEvaluator) Sentence #4 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent4

Text  : Bardzo się ucieszył em , kiedy wszedł em tam pierwszy raz .
Tokens: 1_____ 2__ 3_______ 4_ 5 6____ 7_____ 8_ 9__ 10______ 11_ 12

Chunks:

(ChunkerEvaluator) Sentence #5 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent5

Text  : Moja radość trwała jakieś . . .
Tokens: 1___ 2_____ 3_____ 4_____ 5 6 7

Chunks:

(ChunkerEvaluator) Sentence #6 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent6

Text  : 6 minut - pisze w liście do „ Gazety ”  nasz czytelnik Przemysław Krupiński .
Tokens: 1 2____ 3 4____ 5 6_____ 7_ 8 9_____ 10 11__ 12_______ 13________ 14_______ 15

Chunks:
  TruePositive nam [9,9] = Gazety
  TruePositive nam [13,14] = Przemysław Krupiński

(ChunkerEvaluator) Sentence #7 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent7

Text  : Nie jestem zwolennikiem nieustannego narzekania na to , co dzieje się na kolei .
Tokens: 1__ 2_____ 3___________ 4___________ 5_________ 6_ 7_ 8 9_ 10____ 11_ 12 13___ 14

Chunks:

(ChunkerEvaluator) Sentence #8 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent8

Text  : I naprawdę mam wielki szacunek dla tych wszystkich odpowiedzialnych za remont krakowskiego dworca .
Tokens: 1 2_______ 3__ 4_____ 5_______ 6__ 7___ 8_________ 9_______________ 10 11____ 12__________ 13____ 14

Chunks:

(ChunkerEvaluator) Sentence #9 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent9

Text  : Pięknie to wszystko wygląda na pierwszy rzut oka .
Tokens: 1______ 2_ 3_______ 4______ 5_ 6_______ 7___ 8__ 9

Chunks:

(ChunkerEvaluator) Sentence #10 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent10

Text  : Pachniało nowością .
Tokens: 1________ 2_______ 3

Chunks:

(ChunkerEvaluator) Sentence #11 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent11

Text  : Możecie mi wierzyć lub nie , ale przez chwilę poczuł em się jak na jednym z  niemieckich dworców .
Tokens: 1______ 2_ 3______ 4__ 5__ 6 7__ 8____ 9_____ 10____ 11 12_ 13_ 14 15____ 16 17_________ 18_____ 19

Chunks:

(ChunkerEvaluator) Sentence #12 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent12

Text  : Jakimkolwiek .
Tokens: 1___________ 2

Chunks:

(ChunkerEvaluator) Sentence #13 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent13

Text  : Oni mają tak na każdym .
Tokens: 1__ 2___ 3__ 4_ 5_____ 6

Chunks:

(ChunkerEvaluator) Sentence #14 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent14

Text  : Czyste , jasne płyty na podłodze , kolorowe filary ,  srebrne ławeczki ,  ekrany LCD z  rozkładem jazdy aktualizowanym na bieżąco ,  błękitne tablice LED informujące o  tym ,  jaki pociąg stoi przy peronie .
Tokens: 1_____ 2 3____ 4____ 5_ 6_______ 7 8_______ 9_____ 10 11_____ 12______ 13 14____ 15_ 16 17_______ 18___ 19____________ 20 21_____ 22 23______ 24_____ 25_ 26_________ 27 28_ 29 30__ 31____ 32__ 33__ 34_____ 35

Chunks:
  TruePositive nam [15,15] = LCD
  FalseNegative nam [25,25] = LED

(ChunkerEvaluator) Sentence #15 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent15

Text  : Trochę tylko godzina odjazdu zlewa się z kierunkiem jazdy ,  ale to takie moje czepialstwo .
Tokens: 1_____ 2____ 3______ 4______ 5____ 6__ 7 8_________ 9____ 10 11_ 12 13___ 14__ 15_________ 16

Chunks:

(ChunkerEvaluator) Sentence #16 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent16

Text  : I może moim czepialstwem jest też , że nie jest napisane „  Airport ”  .
Tokens: 1 2___ 3___ 4___________ 5___ 6__ 7 8_ 9__ 10__ 11______ 12 13_____ 14 15

Chunks:
  FalsePositive nam [13,13] = Airport

(ChunkerEvaluator) Sentence #17 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent17

Text  : Albo chociaż „ Lotnisko ” .
Tokens: 1___ 2______ 3 4_______ 5 6

Chunks:
  FalsePositive nam [4,4] = Lotnisko

(ChunkerEvaluator) Sentence #18 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent18

Text  : Naprawdę takie cuda techniki trudno spotkać nawet na innych remontowanych u  nas dworcach .
Tokens: 1_______ 2____ 3___ 4_______ 5_____ 6______ 7____ 8_ 9_____ 10___________ 11 12_ 13______ 14

Chunks:

(ChunkerEvaluator) Sentence #19 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent19

Text  : Ba !
Tokens: 1_ 2

Chunks:

(ChunkerEvaluator) Sentence #20 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent20

Text  : Były nawet donice z kwiatami !
Tokens: 1___ 2____ 3_____ 4 5_______ 6

Chunks:

(ChunkerEvaluator) Sentence #21 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent21

Text  : W życiu nie był em tak szczęśliwym pasażerem !
Tokens: 1 2____ 3__ 4__ 5_ 6__ 7__________ 8________ 9

Chunks:

(ChunkerEvaluator) Sentence #22 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent22

Text  : I dobre kilka minut zajęło mi przejście wzdłuż całego peronu i  napawanie się tym widokiem .
Tokens: 1 2____ 3____ 4____ 5_____ 6_ 7________ 8_____ 9_____ 10____ 11 12_______ 13_ 14_ 15______ 16

Chunks:

(ChunkerEvaluator) Sentence #23 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent23

Text  : Później przeszedł em z powrotem i z każdym krokiem powoli rzedła mi mina
Tokens: 1______ 2________ 3_ 4 5_______ 6 7 8_____ 9______ 10____ 11____ 12 13__

Chunks:

(ChunkerEvaluator) Sentence #24 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent24

Text  : * Na dobry początek automat biletowy .
Tokens: 1 2_ 3____ 4_______ 5______ 6_______ 7

Chunks:

(ChunkerEvaluator) Sentence #25 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent25

Text  : Jedyny na dworcu .
Tokens: 1_____ 2_ 3_____ 4

Chunks:

(ChunkerEvaluator) Sentence #26 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent26

Text  : Sprzedający zresztą wyłącznie bilety na lotnisko .
Tokens: 1__________ 2______ 3________ 4_____ 5_ 6_______ 7

Chunks:

(ChunkerEvaluator) Sentence #27 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent27

Text  : I nieczynny .
Tokens: 1 2________ 3

Chunks:

(ChunkerEvaluator) Sentence #28 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent28

Text  : * Nowy peron to po części obudówka starego .
Tokens: 1 2___ 3____ 4_ 5_ 6_____ 7_______ 8______ 9

Chunks:
  FalsePositive nam [2,3] = Nowy peron

(ChunkerEvaluator) Sentence #29 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent29

Text  : I mimo że ładnie to wygląda , to są miejsca ,  z  których wygląda jeszcze stary peron .
Tokens: 1 2___ 3_ 4_____ 5_ 6______ 7 8_ 9_ 10_____ 11 12 13_____ 14_____ 15_____ 16___ 17___ 18

Chunks:

(ChunkerEvaluator) Sentence #30 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent30

Text  : * Nowe nagłośnienie .
Tokens: 1 2___ 3___________ 4

Chunks:

(ChunkerEvaluator) Sentence #31 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent31

Text  : Ładne .
Tokens: 1____ 2

Chunks:

(ChunkerEvaluator) Sentence #32 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent32

Text  : Trochę kościelne , ale najważniejsza jest przecież jakość .
Tokens: 1_____ 2________ 3 4__ 5____________ 6___ 7_______ 8_____ 9

Chunks:

(ChunkerEvaluator) Sentence #33 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent33

Text  : Czerwona linka między głośnikiem a pomarańczowym filarem to kabel .
Tokens: 1_______ 2____ 3_____ 4_________ 5 6____________ 7______ 8_ 9____ 10

Chunks:

(ChunkerEvaluator) Sentence #34 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent34

Text  : Jedno proste „ ciach ” nożyczkami kuchennymi i nie ma komunikatów z  tego głośnika .
Tokens: 1____ 2_____ 3 4____ 5 6_________ 7_________ 8 9__ 10 11_________ 12 13__ 14______ 15

Chunks:

(ChunkerEvaluator) Sentence #35 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent35

Text  : Ten kabel był w zasięgu mojej wyciągniętej ręki , mierzącego 1  ,  8  m  człowieka .
Tokens: 1__ 2____ 3__ 4 5______ 6____ 7___________ 8___ 9 10________ 11 12 13 14 15_______ 16

Chunks:

(ChunkerEvaluator) Sentence #36 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent36

Text  : Nie musiałem nawet stawać na palcach .
Tokens: 1__ 2_______ 3____ 4_____ 5_ 6______ 7

Chunks:

(ChunkerEvaluator) Sentence #37 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent37

Text  : Jeden z robotników , który przechodził obok mnie , potwierdził nawet moją obawę ,  że faktycznie kabel bez problemu da się przeciąć ,  ale ale !
Tokens: 1____ 2 3_________ 4 5____ 6__________ 7___ 8___ 9 10_________ 11___ 12__ 13___ 14 15 16________ 17___ 18_ 19______ 20 21_ 22______ 23 24_ 25_ 26

Chunks:

(ChunkerEvaluator) Sentence #38 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent38

Text  : Na filarze obok jest kamera , która od razu zarejestruje mój wybryk !
Tokens: 1_ 2______ 3___ 4___ 5_____ 6 7____ 8_ 9___ 10__________ 11_ 12____ 13

Chunks:

(ChunkerEvaluator) Sentence #39 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent39

Text  : Faktycznie .
Tokens: 1_________ 2

Chunks:

(ChunkerEvaluator) Sentence #40 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent40

Text  : Do kamery doskoczy każdy nieznacznie tylko wyższy ode mnie .
Tokens: 1_ 2_____ 3_______ 4____ 5__________ 6____ 7_____ 8__ 9___ 10

Chunks:

(ChunkerEvaluator) Sentence #41 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent41

Text  : Ale dlaczego ja tak pesymistycznie zakładam , że ktoś od razu będzie chciał to wszystko demolować ?
Tokens: 1__ 2_______ 3_ 4__ 5_____________ 6_______ 7 8_ 9___ 10 11__ 12____ 13____ 14 15______ 16_______ 17

Chunks:

(ChunkerEvaluator) Sentence #42 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent42

Text  : Bo zobaczył em kosz na śmieci , który jeszcze tydzień był czysty ,  był w  pełni foremny .
Tokens: 1_ 2_______ 3_ 4___ 5_ 6_____ 7 8____ 9______ 10_____ 11_ 12____ 13 14_ 15 16___ 17_____ 18

Chunks:

(ChunkerEvaluator) Sentence #43 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent43

Text  : Dziś jest troszkę sfatygowany .
Tokens: 1___ 2___ 3______ 4__________ 5

Chunks:

(ChunkerEvaluator) Sentence #44 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent44

Text  : * A propos koszy na śmieci .
Tokens: 1 2 3_____ 4____ 5_ 6_____ 7

Chunks:

(ChunkerEvaluator) Sentence #45 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent45

Text  : Nie wiem , kto odpowiada za czystość na peronie i  w  jaki sposób o  nią dba ,  ale jeżeli zabrudzanie peronu będzie postępowało w  takim tempie ,  to nim oddany zostanie ostatni peron ,  ten pierwszy będzie wyglądał tak ,  jak obecnie wygląda peron piąty .
Tokens: 1__ 2___ 3 4__ 5________ 6_ 7_______ 8_ 9______ 10 11 12__ 13____ 14 15_ 16_ 17 18_ 19____ 20_________ 21____ 22____ 23_________ 24 25___ 26____ 27 28 29_ 30____ 31______ 32_____ 33___ 34 35_ 36______ 37____ 38______ 39_ 40 41_ 42_____ 43_____ 44___ 45___ 46

Chunks:

(ChunkerEvaluator) Sentence #46 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent46

Text  : * Rozkład jazdy widnieje na elektronicznych wyświetlaczach , na wszelki wypadek na peronie wisi też tradycyjny rozkład ,  zamontowany w  tradycyjny dla nas sposób .
Tokens: 1 2______ 3____ 4_______ 5_ 6______________ 7_____________ 8 9_ 10_____ 11_____ 12 13_____ 14__ 15_ 16________ 17_____ 18 19_________ 20 21________ 22_ 23_ 24____ 25

Chunks:

(ChunkerEvaluator) Sentence #47 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent47

Text  : * Strasznie spodobała mi się nowa szklana osłona peronu zbudowana między nim a  Galerią Krakowską (  zdjęcie 9  )  .
Tokens: 1 2________ 3________ 4_ 5__ 6___ 7______ 8_____ 9_____ 10_______ 11____ 12_ 13 14_____ 15_______ 16 17_____ 18 19 20

Chunks:
  TruePositive nam [14,15] = Galerią Krakowską

(ChunkerEvaluator) Sentence #48 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent48

Text  : Fajny pomysł .
Tokens: 1____ 2_____ 3

Chunks:

(ChunkerEvaluator) Sentence #49 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent49

Text  : Tylko że już teraz te szyby z zewnątrz są w  niektórych miejscach tak brudne ,  jakby ktoś na nie zwymiotował .
Tokens: 1____ 2_ 3__ 4____ 5_ 6____ 7 8_______ 9_ 10 11________ 12_______ 13_ 14____ 15 16___ 17__ 18 19_ 20_________ 21

Chunks:

(ChunkerEvaluator) Sentence #50 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent50

Text  : A nie sądzę , żeby regularnie co tydzień ktoś je czyścił .
Tokens: 1 2__ 3____ 4 5___ 6_________ 7_ 8______ 9___ 10 11_____ 12

Chunks:

(ChunkerEvaluator) Sentence #51 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent51

Text  : I nie wiem , czy trzeba być jakimś przeogromnym pedantem i  estetą ,  żeby przeszkadzało to ,  że w  miejscu ,  w  którym stoi słup trakcyjny ,  nie ma szyby ,  tylko jest .  .  .  dziura (  zdjęcie 10 )  .
Tokens: 1 2__ 3___ 4 5__ 6_____ 7__ 8_____ 9___________ 10______ 11 12____ 13 14__ 15___________ 16 17 18 19 20_____ 21 22 23____ 24__ 25__ 26_______ 27 28_ 29 30___ 31 32___ 33__ 34 35 36 37____ 38 39_____ 40 41 42

Chunks:

(ChunkerEvaluator) Sentence #52 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent52

Text  : Deszcz na peronie pierwszym będzie więc padał wybiórczo , tylko w  niektórych sektorach .
Tokens: 1_____ 2_ 3______ 4________ 5_____ 6___ 7____ 8________ 9 10___ 11 12________ 13_______ 14

Chunks:

(ChunkerEvaluator) Sentence #53 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent53

Text  : * Chciał by m też zwrócić uwagę na mocowanie tabliczki numerami peronu i  sektorów .
Tokens: 1 2_____ 3_ 4 5__ 6______ 7____ 8_ 9________ 10_______ 11______ 12____ 13 14______ 15

Chunks:

(ChunkerEvaluator) Sentence #54 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent54

Text  : Wszystkie , na całym peronie , zawieszone są na srebrnych łańcuszkach .
Tokens: 1________ 2 3_ 4____ 5______ 6 7_________ 8_ 9_ 10_______ 11_________ 12

Chunks:

(ChunkerEvaluator) Sentence #55 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent55

Text  : Niemal takich , jakie dostaje się na Komunię .
Tokens: 1_____ 2_____ 3 4____ 5______ 6__ 7_ 8______ 9

Chunks:
  TruePositive nam [8,8] = Komunię

(ChunkerEvaluator) Sentence #56 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent56

Text  : Pięknie to wygląda .
Tokens: 1______ 2_ 3______ 4

Chunks:

(ChunkerEvaluator) Sentence #57 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent57

Text  : Naprawdę .
Tokens: 1_______ 2

Chunks:

(ChunkerEvaluator) Sentence #58 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent58

Text  : Przy najmniejszym podmuchu wiatru tabliczki elegancko się bujają , a  przy większym wietrze ruszają się na tyle ,  że na przykład słów „  track ”  i  „  sector ”  nie ma szans przeczytać .
Tokens: 1___ 2___________ 3_______ 4_____ 5________ 6________ 7__ 8_____ 9 10 11__ 12______ 13_____ 14_____ 15_ 16 17__ 18 19 20 21______ 22__ 23 24___ 25 26 27 28____ 29 30_ 31 32___ 33________ 34

Chunks:

(ChunkerEvaluator) Sentence #59 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent59

Text  : A na tabliczkę informującą o niebezpieczeństwie związanym z napięciem w  trakcji chyba zabrakło łańcuszka ,  bo jako jedyna zawisła troszeczkę krzywo .
Tokens: 1 2_ 3________ 4__________ 5 6_________________ 7________ 8 9________ 10 11_____ 12___ 13______ 14_______ 15 16 17__ 18____ 19_____ 20________ 21____ 22

Chunks:

(ChunkerEvaluator) Sentence #60 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent60

Text  : Ale to nikomu nie chyba nie przeszkadza .
Tokens: 1__ 2_ 3_____ 4__ 5____ 6__ 7__________ 8

Chunks:

(ChunkerEvaluator) Sentence #61 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent61

Text  : * Jest jeszcze jedna tabliczka informacyjna na peronie .
Tokens: 1 2___ 3______ 4____ 5________ 6___________ 7_ 8______ 9

Chunks:

(ChunkerEvaluator) Sentence #62 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent62

Text  : W zasadzie jedyna , jaka się na nim pojawia .
Tokens: 1 2_______ 3_____ 4 5___ 6__ 7_ 8__ 9______ 10

Chunks:

(ChunkerEvaluator) Sentence #63 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent63

Text  : We Włoszech wszystkie drogi prowadzą do Rzymu , a u  nas wszystkie drogi prowadzą do centrum .
Tokens: 1_ 2_______ 3________ 4____ 5_______ 6_ 7____ 8 9 10 11_ 12_______ 13___ 14______ 15 16_____ 17

Chunks:
  TruePositive nam [2,2] = Włoszech
  TruePositive nam [7,7] = Rzymu

(ChunkerEvaluator) Sentence #64 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent64

Text  : Potrzeba taksówki ?
Tokens: 1_______ 2_______ 3

Chunks:

(ChunkerEvaluator) Sentence #65 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent65

Text  : Zapraszamy do centrum .
Tokens: 1_________ 2_ 3______ 4

Chunks:

(ChunkerEvaluator) Sentence #66 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent66

Text  : Toaleta ?
Tokens: 1______ 2

Chunks:

(ChunkerEvaluator) Sentence #67 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent67

Text  : Do centrum .
Tokens: 1_ 2______ 3

Chunks:

(ChunkerEvaluator) Sentence #68 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent68

Text  : Na lotnisko ?
Tokens: 1_ 2_______ 3

Chunks:

(ChunkerEvaluator) Sentence #69 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent69

Text  : Też do centrum .
Tokens: 1__ 2_ 3______ 4

Chunks:

(ChunkerEvaluator) Sentence #70 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent70

Text  : A w centrum będzie informacja , że trzeba wrócić na peron pierwszy .
Tokens: 1 2 3______ 4_____ 5_________ 6 7_ 8_____ 9_____ 10 11___ 12______ 13

Chunks:

(ChunkerEvaluator) Sentence #71 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent71

Text  : Ale OK !
Tokens: 1__ 2_ 3

Chunks:

(ChunkerEvaluator) Sentence #72 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent72

Text  : Zawsze mieli śmy problemy z prawidłowym oznakowaniem , więc poradzili śmy sobie i  teraz .
Tokens: 1_____ 2____ 3__ 4_______ 5 6__________ 7___________ 8 9___ 10_______ 11_ 12___ 13 14___ 15

Chunks:

(ChunkerEvaluator) Sentence #73 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent73

Text  : Ale że po angielsku jest napisane „ Exi to Center ”  !  ?  !  ?
Tokens: 1__ 2_ 3_ 4________ 5___ 6_______ 7 8__ 9_ 10____ 11 12 13 14 15

Chunks:
  FalsePositive nam [8,10] = Exi to Center

(ChunkerEvaluator) Sentence #74 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent74

Text  : I to na każdej tabliczce !
Tokens: 1 2_ 3_ 4_____ 5________ 6

Chunks:

(ChunkerEvaluator) Sentence #75 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent75

Text  : Jedna literka , a obciach większy , niż jakby ktoś się wyłożył na konstrukcji gramatycznej całego zdania !
Tokens: 1____ 2______ 3 4 5______ 6______ 7 8__ 9____ 10__ 11_ 12_____ 13 14_________ 15__________ 16____ 17____ 18

Chunks:

(ChunkerEvaluator) Sentence #76 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent76

Text  : To właśnie w tym tkwi nasz problem .
Tokens: 1_ 2______ 3 4__ 5___ 6___ 7______ 8

Chunks:

(ChunkerEvaluator) Sentence #77 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent77

Text  : W szczegółach .
Tokens: 1 2__________ 3

Chunks:

(ChunkerEvaluator) Sentence #78 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent78

Text  : Kilka takich niedociągnięć i cały wysiłek leży .
Tokens: 1____ 2_____ 3____________ 4 5___ 6______ 7___ 8

Chunks:

(ChunkerEvaluator) Sentence #79 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent79

Text  : I te tabliczki tak sobie będą już wisieć .
Tokens: 1 2_ 3________ 4__ 5____ 6___ 7__ 8_____ 9

Chunks:

(ChunkerEvaluator) Sentence #80 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent80

Text  : I zawisną też pewnie na pozostałych peronach .
Tokens: 1 2______ 3__ 4_____ 5_ 6__________ 7_______ 8

Chunks:

(ChunkerEvaluator) Sentence #81 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent81

Text  : A ktoś , kto będzie je wieszał i też to zauważy ,  pomyśli sobie pewnie ,  że to nie jemu płacą za to ,  żeby było poprawnie .
Tokens: 1 2___ 3 4__ 5_____ 6_ 7______ 8 9__ 10 11_____ 12 13_____ 14___ 15____ 16 17 18 19_ 20__ 21___ 22 23 24 25__ 26__ 27_______ 28

Chunks:

(ChunkerEvaluator) Sentence #82 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent82

Text  : Panie i Panowie wykonawcy !
Tokens: 1____ 2 3______ 4________ 5

Chunks:

(ChunkerEvaluator) Sentence #83 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent83

Text  : Mimo tych wszystkich drobnych niedociągnięć naprawdę jestem dumny z tego ,  co zrobili ście .
Tokens: 1___ 2___ 3_________ 4_______ 5____________ 6_______ 7_____ 8____ 9 10__ 11 12 13_____ 14__ 15

Chunks:

(ChunkerEvaluator) Sentence #84 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent84

Text  : Naprawdę ten dworzec będzie wyglądał europejsko , naprawdę pobije na łeb na szyję warszawski centralny .
Tokens: 1_______ 2__ 3______ 4_____ 5_______ 6_________ 7 8_______ 9_____ 10 11_ 12 13___ 14________ 15_______ 16

Chunks:

(ChunkerEvaluator) Sentence #85 from /nlp/corpora/agora/agora-workdir/agora-1.2.1-names-disamb-nam/articles/00107376.xml from sent85

Text  : Tylko czy ktoś ( bardzo proszę ) może wyciągnąć troszkę wniosków z  tego ,  co tu napisał em ,  przy remoncie przynajmniej jednego z  kolejnych peronów ?
Tokens: 1____ 2__ 3___ 4 5_____ 6_____ 7 8___ 9________ 10_____ 11______ 12 13__ 14 15 16 17_____ 18 19 20__ 21______ 22__________ 23_____ 24 25_______ 26_____ 27

Chunks:

======================================================================================
# Exact match evaluation -- annotation span and types evaluation
======================================================================================
        Annotation                     &      TP &      FP &      FN & Precision & Recall  & F$_1$   \\
\hline
        nam                            &       8 &       4 &       1 &    66.67% &  88.89% &  76.19% \\
\hline
        *TOTAL*                        &       8 &       4 &       1 &    66.67% &  88.89% &  76.19% \\


======================================================================================
# Annotation span evaluation (annotation types are ignored)
======================================================================================
        Annotation                     &      TP &      FP &      FN & Precision & Recall  & F$_1$   \\
\hline
        *TOTAL*                        &       8 &       4 &       1 &    66.67% &  88.89% &  76.19% \\


======================================================================================
# MUC match evaluation
======================================================================================
        Annotation                     &     COR &     ACT &     POS & Precision & Recall  & F$_1$   \\
\hline
        nam                            &      16 &       8 &       2 &    66.67% &  88.89% &  76.19% \\
\hline
        *TOTAL*                        &      16 &       8 &       2 &    66.67% &  88.89% &  76.19% \\


====================================================
Processing time
====================================================
1) Model loading        : --h --m 00s (58264178ns) 
2) Data reading         : --h --m 00s (10386ns) 
3) Feature generation   : --h --m 00s (107776518ns) 
4) Chunking             : --h --m 00s (196146704ns) 
5) Evaluation           : --h --m 00s (337072372ns) (not in total time)
----------------------------------------------------
## Total time             --h --m 00s (362197786ns)
----------------------------------------------------
Tokens           :     1007
Text kB          :        5.57
Tokens  / second :     2780.25
Text kB / second :       15.37
----------------------------------------------------
