{"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/evaluating-the-utility-of-hand-crafted","title":"Evaluating the Utility of Hand-crafted Features in Sequence Labelling","arxiv_id":"1808.09075","date":"2018-08-28","proceeding":"EMNLP 2018 10","authors":["Minghao Wu","Fei Liu","Trevor Cohn"],"abstract":"Conventional wisdom is that hand-crafted features are redundant for deep\nlearning models, as they already learn adequate representations of text\nautomatically from corpora. In this work, we test this claim by proposing a new\nmethod for exploiting handcrafted features as part of a novel hybrid learning\napproach, incorporating a feature auto-encoder loss component. We evaluate on\nthe task of named entity recognition (NER), where we show that including manual\nfeatures for part-of-speech, word shapes and gazetteers can improve the\nperformance of a neural CRF model. We obtain a $F_1$ of 91.89 for the\nCoNLL-2003 English shared task, which significantly outperforms a collection of\nhighly competitive baseline models. We also present an ablation study showing\nthe importance of auto-encoding, over using features as either inputs or\noutputs alone, and moreover, show including the autoencoder components reduces\ntraining requirements to 60\\%, while retaining the same predictive accuracy.","url_abs":"http://arxiv.org/abs/1808.09075v1","url_pdf":"http://arxiv.org/pdf/1808.09075v1.pdf","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":[{"paper_slug":"evaluating-the-utility-of-hand-crafted","repo_url":"https://github.com/minghao-wu/CRF-AE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"cg","task_name":"NER"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[{"method_slug":"crf","method_name":"CRF"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/named-entity-recognition-ner-on-conll-2003","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2003 (English)","model":"Neural-CRF+AE","rank_in_archive_order":45,"of":73,"metrics":{"F1":"92.29"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-ner-on-conll-2003","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2003 (English)","model":"CRF + AutoEncoder","rank_in_archive_order":52,"of":73,"metrics":{"F1":"91.87"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1808.09075","atlas_url":"https://app.syntology.ai/?focus=1808.09075","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.09075"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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