{"doi":"10.18653/v1/k19-1018","title":"Generating Timelines by Modeling Semantic Change","abstract":null,"journal":"Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)","year":2019,"id":639593,"datarank":0.43848349428665645,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.14659697192835938,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.14659697192835938,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":4,"citers_with_citation_signal":4,"citers_with_endowment":4,"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":1661840,"name":"Kira Radinsky","orcid":null,"position":1,"is_corresponding":false},{"id":1661839,"name":"Guy D. Rosin","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Generating Timelines by Modeling Semantic Change","abstract":"Though languages can evolve slowly, they can also react strongly to dramatic world events. By studying the connection between words and events, it is possible to identify which events change our vocabulary and in what way. In this work, we tackle the task of creating timelines-records of historical 'turning points', represented by either words or events, to understand the dynamics of a target word. Our approach identifies these points by leveraging both static and time-varying word embeddings to measure the influence of words and events. In addition to quantifying changes, we show how our technique can help isolate semantic changes. Our qualitative and quantitative evaluations show that we are able to capture this semantic change and event influence.","is_dataset_classified":null,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"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/W2989063751","authors":[],"funders":[],"total_grants":0,"fwci":15.8396,"citation_percentile":0.99054374,"influential_citations":0,"citation_trend":[{"year":2020,"count":1},{"year":2021,"count":4},{"year":2022,"count":1}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.aclweb.org/anthology/K19-1018.pdf","host_type":""},{"url":"https://www.aclweb.org/anthology/K19-1018.pdf","host_type":""},{"url":"https://doi.org/10.18653/v1/k19-1018","host_type":""}],"fields_of_study":["Language and cultural evolution","Advanced Text Analysis Techniques","Evolutionary Game Theory and Cooperation"],"mesh_terms":[],"keywords":["Timeline","Computer science","Vocabulary","Task (project management)","Semantic change","Word (group theory)","Event (particle physics)","Natural language processing","Dynamics (music)","Artificial intelligence","Semantics (computer science)","Linguistics"],"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":"2026-08-07T00:55:35.596107Z","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":[]}