{"doi":"10.3115/992730.992735","title":"Tagging and chunking with bigrams","abstract":null,"journal":"Proceedings of the 18th conference on Computational linguistics  -","year":2000,"id":604073,"datarank":0.47032413238937254,"base_score":3.1354942159291497,"endowment":3.1354942159291497,"self_citation_contribution":0.47032413238937254,"citation_network_contribution":0.0,"self_endowment_contribution":0.47032413238937254,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":22,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1372114,"name":"Antonio Molina","orcid":"0000-0003-3137-7938","position":1,"is_corresponding":false},{"id":1549780,"name":"Natividad Prieto","orcid":null,"position":2,"is_corresponding":false},{"id":1549778,"name":"Ferran Pla","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Tagging and chunking with bigrams","abstract":"In this paper we present an integrated system for tagging and chunking texts from a certain language. The approach is based on stochastic finite-state models that are learnt automatically. This includes biagram models or finite-state automata learnt using grammatical inference techniques. As the models involved in our system are learnt automatically, this is a very flexible and portable system.In order to show the viability of our approach we present results for tagging and chunking using bigram models on the Wall Street Journal corpus. We have achieved an accuracy rate for tagging of 96.8%, and a precision rate for NP chunks of 94.6% with a recall rate of 93.6%.","is_dataset_classified":null,"base_score":3.1354942159291497,"endowment":3.1354942159291497,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"23304386","pmcid":null,"openalex_id":"https://openalex.org/W2001108429","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2012,"count":1},{"year":2018,"count":1}],"oa_status":"gold","license":null,"oa_locations":[{"url":"https://dl.acm.org/doi/pdf/10.3115/992730.992735","host_type":""},{"url":"https://dl.acm.org/doi/pdf/10.3115/992730.992735","host_type":""},{"url":"https://doi.org/10.3115/992730.992735","host_type":""},{"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.8743","host_type":""},{"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.581.4935","host_type":""}],"fields_of_study":["Natural Language Processing Techniques","Topic Modeling","Algorithms and Data Compression"],"mesh_terms":[],"keywords":["Bigram","Chunking (psychology)","Computer science","Natural language processing","Artificial intelligence","Inference","Recall rate","Computational linguistics","Language model","Precision and recall","Machine learning"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Quality Education"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T23:18:12.867089Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}