{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/no-means-no-a-non-im-proper-modeling-approach","title":"No means ‘No’; a non-im-proper modeling approach, with embedded speculative context","arxiv_id":null,"date":"2022-08-30","proceeding":"Bioinformatics 2022 8","authors":["Priya Tiwary","Akshayraj M","Amit Gautam"],"abstract":"Motivation: The medical data are complex in nature as terms that appear in records usually appear in different contexts. Through this paper, we investigate various bio model’s embeddings(BioBERT, BioE- LECTRA, PubMedBERT) on their understanding of \"negation and speculation context\" wherein we found that these models were unable to differentiate \"negated context\" vs \"non-negated context\". To measure the understanding of models, we used cosine similarity scores of negated sentence embeddings vs non- negated sentence embeddings pairs. For improving these models, we introduce a generic super tuning approach to enhance the embeddings on \"negation and speculation context\" by utilizing a synthesized dataset.\r\nResults: After super-tuning the models we can see that the model’s embeddings are now understanding negative and speculative contexts much better. Furthermore, we fine-tuned the super tuned models on various tasks and we found that the model has outperformed the previous models and achieved state- of-the-art (SOTA) on negation, speculation cue, and scope detection tasks on BioScope abstracts and Sherlock dataset. We also confirmed that our approach had a very minimal trade-off in the performance of the model in other tasks like Natural Language Inference after super-tuning.","url_abs":"https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btac593/6678982","url_pdf":"https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btac593/6678982","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"negation","task_name":"Negation"},{"task_slug":"negation-scope-resolution","task_name":"Negation Scope Resolution"},{"task_slug":"negation-and-speculation-cue-detection","task_name":"Negation and Speculation Cue Detection"},{"task_slug":"negation-and-speculation-scope-resolution","task_name":"Negation and Speculation Scope resolution"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embeddings","task_name":"Sentence Embeddings"},{"task_slug":"speculation-scope-resolution","task_name":"Speculation Scope Resolution"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"electra","method_name":"ELECTRA"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/negation-scope-resolution-on-sem-2012-shared","task":"Negation Scope Resolution","dataset":"*sem 2012 Shared Task: Sherlock Dataset","model":"NegBioELECTRA","rank_in_archive_order":1,"of":3,"metrics":{"F1":"97.26"},"uses_additional_data":false},{"leaderboard":"/sota/negation-scope-resolution-on-bioscope","task":"Negation Scope Resolution","dataset":"BioScope : Abstracts","model":"NegBioELECTRA","rank_in_archive_order":1,"of":3,"metrics":{"F1":"98.94"},"uses_additional_data":false},{"leaderboard":"/sota/negation-and-speculation-cue-detection-on-sem","task":"Negation and Speculation Cue Detection","dataset":"*sem 2012 Shared Task: Sherlock Dataset","model":"NegBioELECTRA","rank_in_archive_order":1,"of":2,"metrics":{"F1":"99.56"},"uses_additional_data":false},{"leaderboard":"/sota/negation-and-speculation-cue-detection-on","task":"Negation and Speculation Cue Detection","dataset":"BioScope : Abstracts","model":"NegBioELECTRA","rank_in_archive_order":1,"of":1,"metrics":{"F1":"99.02"},"uses_additional_data":false},{"leaderboard":"/sota/speculation-scope-resolution-on-bioscope","task":"Speculation Scope Resolution","dataset":"BioScope : Abstracts","model":"NegBioELECTRA","rank_in_archive_order":1,"of":2,"metrics":{"F1":"98.37"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}