{"doi":"10.1016/j.proeng.2012.01.242","title":"A Recognition Approach Study on Chinese Field Term Based Mutual Information /Conditional Random Fields","abstract":null,"journal":"Procedia Engineering","year":2012,"id":636958,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"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":1653612,"name":"Zong-tian Liu","orcid":null,"position":1,"is_corresponding":false},{"id":1653614,"name":"Li-min Zhang","orcid":null,"position":2,"is_corresponding":false},{"id":1653610,"name":"Lin Peng","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"A Recognition Approach Study on Chinese Field Term Based Mutual Information /Conditional Random Fields","abstract":"A new auto-recognition approach based on mutual information/ Conditional Random Fields (CRFs) was put forward in this study. Firstly, statistics-based mutual information algorithm was applied to separate the Chinese words accurately, then the sub-words were picked out from the accurate separation according to the entropy of the left and right information. Secondly, the relative frequency of the sub-words was calculated. Thirdly, three training characteristics, including words, part of speech and relative frequency, were used as training datasets to obtain a model for field terms characters by CRFs. Thirdly, the Chinese words recognition was accomplished by the CRFs model. Finally, a practical experiment was executed and the results showed that the precision, percentage and Fmeasure of the recognition is 78.63%, 87.10% and 82.65% respectively, which is significant better the normal mutual information/ Conditional Random Fields (CRFs) algorithm.","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W2011251309","authors":[],"funders":[],"total_grants":0,"fwci":1.0941,"citation_percentile":0.71986223,"influential_citations":0,"citation_trend":[{"year":2013,"count":2},{"year":2025,"count":1}],"oa_status":"gold","license":"cc-by-nc-nd","oa_locations":[{"url":"https://doi.org/10.1016/j.proeng.2012.01.242","host_type":"journal"},{"url":"https://doi.org/10.1016/j.proeng.2012.01.242","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S1877705812002524?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S1877705812002524?httpAccept=text/plain","host_type":"publisher"}],"fields_of_study":["Advanced Algorithms and Applications","Remote Sensing and Land Use","Traditional Chinese Medicine Studies"],"mesh_terms":[],"keywords":["CRFS","Conditional random field","Conditional entropy","Mutual information","Term (time)","Computer science","Artificial intelligence","Entropy (arrow of time)","Pattern recognition (psychology)","Conditional mutual information","Speech recognition","Field (mathematics)","Natural language processing","Mathematics","Principle of maximum entropy"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Peace, Justice and strong institutions"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T18:03:11.109536Z","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":[]}