{"id":"https://openalex.org/W2751275290","doi":"https://doi.org/10.18653/v1/d17-1313","title":"Learning what to read: Focused machine reading","display_name":"Learning what to read: Focused machine reading","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2751275290","doi":"https://doi.org/10.18653/v1/d17-1313","mag":"2751275290"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d17-1313","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1313","pdf_url":"https://www.aclweb.org/anthology/D17-1313.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D17-1313.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042363520","display_name":"Enrique Noriega-Atala","orcid":"https://orcid.org/0000-0001-7150-2989"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Enrique Noriega-Atala","raw_affiliation_strings":["University of Arizona Tucson, Arizona, USA","University of Arizona"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona Tucson, Arizona, USA","institution_ids":["https://openalex.org/I138006243"]},{"raw_affiliation_string":"University of Arizona","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066447249","display_name":"Marco A. Valenzuela-Esc\u00e1rcega","orcid":null},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marco A. Valenzuela-Esc\u00e1rcega","raw_affiliation_strings":["University of Arizona"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017273810","display_name":"Clayton T. Morrison","orcid":"https://orcid.org/0000-0002-3606-0078"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]},{"id":"https://openalex.org/I4210139749","display_name":"Engineering Arts (United States)","ror":"https://ror.org/04g1y2z78","country_code":"US","type":"company","lineage":["https://openalex.org/I4210139749"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Clayton Morrison","raw_affiliation_strings":["University of Arizona Tucson, Arizona, USA","Sciences Technology and Arts, School of"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona Tucson, Arizona, USA","institution_ids":["https://openalex.org/I138006243"]},{"raw_affiliation_string":"Sciences Technology and Arts, School of","institution_ids":["https://openalex.org/I4210139749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047699502","display_name":"Mihai Surdeanu","orcid":"https://orcid.org/0000-0001-6956-8030"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]},{"id":"https://openalex.org/I4210139749","display_name":"Engineering Arts (United States)","ror":"https://ror.org/04g1y2z78","country_code":"US","type":"company","lineage":["https://openalex.org/I4210139749"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mihai Surdeanu","raw_affiliation_strings":["University of Arizona Tucson, Arizona, USA","Sciences Technology and Arts, School of"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona Tucson, Arizona, USA","institution_ids":["https://openalex.org/I138006243"]},{"raw_affiliation_string":"Sciences Technology and Arts, School of","institution_ids":["https://openalex.org/I4210139749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.126658,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2905","last_page":"2910"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13937","display_name":"Genetics, Bioinformatics, and Biomedical Research","score":0.9470999836921692,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12478","display_name":"Wikis in Education and Collaboration","score":0.9435999989509583,"subfield":{"id":"https://openalex.org/subfields/3315","display_name":"Communication"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.8642700910568237},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8307610750198364},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6692514419555664},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6579726934432983},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.6227734684944153},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5666062235832214},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5431647896766663},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.47789037227630615},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41067665815353394},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3759176433086395},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.0999951958656311},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.057834357023239136}],"concepts":[{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.8642700910568237},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8307610750198364},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6692514419555664},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6579726934432983},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.6227734684944153},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5666062235832214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5431647896766663},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.47789037227630615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41067665815353394},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3759176433086395},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0999951958656311},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.057834357023239136},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.18653/v1/d17-1313","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1313","pdf_url":"https://www.aclweb.org/anthology/D17-1313.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1709.00149","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1709.00149","pdf_url":"https://arxiv.org/pdf/1709.00149","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.1709.00149","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1709.00149","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"},{"id":"mag:2751275290","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.18653/v1/d17-1313","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1313","pdf_url":"https://www.aclweb.org/anthology/D17-1313.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8799999952316284,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G3048660544","display_name":null,"funder_award_id":"W911NF-14-1-","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G3424441068","display_name":null,"funder_award_id":"W911NF-14-1","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G5111240050","display_name":null,"funder_award_id":"W911NF-14-1-0395","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320310160","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2751275290.pdf","grobid_xml":"https://content.openalex.org/works/W2751275290.grobid-xml"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W2032566933","https://openalex.org/W2054473834","https://openalex.org/W2251392452","https://openalex.org/W2273647512","https://openalex.org/W2312609093","https://openalex.org/W2526182867","https://openalex.org/W2916355958","https://openalex.org/W2952420254"],"related_works":["https://openalex.org/W2963380037","https://openalex.org/W1544827683","https://openalex.org/W3178406120","https://openalex.org/W3158683309","https://openalex.org/W2409591106","https://openalex.org/W3098852125","https://openalex.org/W48501643","https://openalex.org/W2125436846","https://openalex.org/W2904097689","https://openalex.org/W2963884026","https://openalex.org/W2914331073","https://openalex.org/W2761849348","https://openalex.org/W2893597360","https://openalex.org/W3172747961","https://openalex.org/W589128720","https://openalex.org/W1693085440","https://openalex.org/W3150804300","https://openalex.org/W3033176962","https://openalex.org/W2149397565","https://openalex.org/W2913975612"],"abstract_inverted_index":{"Recent":[0],"efforts":[1],"in":[2,8],"bioinformatics":[3],"have":[4],"achieved":[5],"tremendous":[6],"progress":[7],"the":[9,16,19,69,121,130,140],"machine":[10,33,70],"reading":[11,34,65,71,147],"of":[12,18,35,72,94,135],"biomedical":[13,73,84],"literature,":[14],"and":[15,55,104],"assembly":[17],"extracted":[20],"biochemical":[21],"interactions":[22],"into":[23],"large-scale":[24],"models":[25],"such":[26],"as":[27,86,88],"protein":[28],"signaling":[29],"pathways.":[30],"However,":[31],"batch":[32],"literature":[36,74,77],"at":[37],"today's":[38],"scale":[39],"(PubMed":[40],"alone":[41],"indexes":[42],"over":[43],"1":[44],"million":[45],"papers":[46],"per":[47],"year)":[48],"is":[49,133],"unfeasible":[50],"due":[51],"to":[52,67,81,118],"both":[53],"cost":[54],"processing":[56],"overhead.":[57],"In":[58],"this":[59],"work,":[60],"we":[61],"introduce":[62,91],"a":[63,83,92,105,110],"focused":[64,97],"approach":[66,107,132],"guide":[68],"towards":[75],"what":[76],"should":[78],"be":[79],"read":[80],"answer":[82],"query":[85],"efficiently":[87],"possible.":[89],"We":[90,127],"family":[93],"algorithms":[95],"for":[96],"reading,":[98],"including":[99],"an":[100],"intuitive,":[101],"strong":[102],"baseline,":[103,141],"second":[106],"which":[108],"uses":[109],"reinforcement":[111],"learning":[112],"(RL)":[113],"framework":[114],"that":[115,129],"learns":[116],"when":[117],"explore":[119],"(widen":[120],"search)":[122],"or":[123],"exploit":[124],"(narrow":[125],"it).":[126],"demonstrate":[128],"RL":[131],"capable":[134],"answering":[136],"more":[137,144],"queries":[138],"than":[139],"while":[142],"being":[143],"efficient,":[145],"i.e.,":[146],"fewer":[148],"documents.":[149]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
