{"id":"https://openalex.org/W7077487194","doi":"https://doi.org/10.48550/arxiv.2508.15837","title":"Statistical Comparative Analysis of Semantic Similarities and Model Transferability Across Datasets for Short Answer Grading","display_name":"Statistical Comparative Analysis of Semantic Similarities and Model Transferability Across Datasets for Short Answer Grading","publication_year":2025,"publication_date":"2025-08-19","ids":{"openalex":"https://openalex.org/W7077487194","doi":"https://doi.org/10.48550/arxiv.2508.15837"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2508.15837","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.15837","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2508.15837","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bonthu, Sridevi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bonthu, Sridevi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Sree, S. Rama","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sree, S. Rama","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Prasad, M. H. M. Krishna","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prasad, M. H. M. Krishna","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.4088999927043915,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.4088999927043915,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13177","display_name":"Geological and Geophysical Studies","score":0.039500001817941666,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13067","display_name":"Geological Modeling and Analysis","score":0.033900000154972076,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7465000152587891},{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.6894000172615051},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.6195999979972839},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.43369999527931213},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.43160000443458557},{"id":"https://openalex.org/keywords/grading","display_name":"Grading (engineering)","score":0.40790000557899475},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4052000045776367}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7465000152587891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7415000200271606},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.6894000172615051},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.6195999979972839},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5491999983787537},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5303000211715698},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.43369999527931213},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C2777286243","wikidata":"https://www.wikidata.org/wiki/Q5591926","display_name":"Grading (engineering)","level":2,"score":0.40790000557899475},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.3882000148296356},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.375900000333786},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.31700000166893005},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2563999891281128},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2508.15837","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.15837","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2508.15837","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.15837","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Developing":[0],"dataset-specific":[1,158],"models":[2,21,41,143],"involves":[3],"iterative":[4],"fine-tuning":[5],"and":[6,66,87,114,165],"optimization,":[7],"incurring":[8],"significant":[9],"costs":[10],"over":[11],"time.":[12],"This":[13,147],"study":[14],"investigates":[15],"the":[16,36,64,72,79,111,125,129,138,154,167],"transferability":[17],"of":[18,58,94,102,116,121,131],"state-of-the-art":[19],"(SOTA)":[20],"trained":[22],"on":[23,52],"established":[24],"datasets":[25,44,96],"to":[26,48,106,127,140,150],"an":[27],"unexplored":[28,80],"text":[29],"dataset.":[30],"The":[31,99,119],"key":[32],"question":[33],"is":[34,97,105],"whether":[35],"knowledge":[37],"embedded":[38],"within":[39],"SOTA":[40,117],"from":[42],"existing":[43,142],"can":[45],"be":[46],"harnessed":[47],"achieve":[49],"high-performance":[50],"results":[51],"a":[53,90,151],"new":[54],"domain.":[55,81],"In":[56],"pursuit":[57],"this":[59,103,122],"inquiry,":[60],"two":[61],"well-established":[62],"benchmarks,":[63],"STSB":[65],"Mohler":[67],"datasets,":[68],"are":[69],"selected,":[70],"while":[71],"recently":[73],"introduced":[74],"SPRAG":[75],"dataset":[76],"serves":[77],"as":[78],"By":[82],"employing":[83],"robust":[84],"similarity":[85],"metrics":[86],"statistical":[88],"techniques,":[89],"meticulous":[91],"comparative":[92],"analysis":[93],"these":[95],"conducted.":[98],"primary":[100],"goal":[101],"work":[104],"yield":[107],"comprehensive":[108],"insights":[109],"into":[110],"potential":[112,126],"applicability":[113],"adaptability":[115],"models.":[118],"outcomes":[120],"research":[123],"have":[124],"reshape":[128],"landscape":[130],"natural":[132],"language":[133],"processing":[134],"(NLP)":[135],"by":[136],"unlocking":[137],"ability":[139],"leverage":[141],"for":[144,156,169],"diverse":[145],"datasets.":[146],"may":[148],"lead":[149],"reduction":[152],"in":[153,163],"demand":[155],"resource-intensive,":[157],"training,":[159],"thereby":[160],"accelerating":[161],"advancements":[162],"NLP":[164],"paving":[166],"way":[168],"more":[170],"efficient":[171],"model":[172],"deployment.":[173]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
