{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:LEOJKIYT55Y3A5SAO6IMRSD2ZM","short_pith_number":"pith:LEOJKIYT","schema_version":"1.0","canonical_sha256":"591c952313ef71b076407790c8c87acb0e6dd027e370928f7eeeb437fd85d50b","source":{"kind":"arxiv","id":"1606.01280","version":4},"attestation_state":"computed","paper":{"title":"Dependency Parsing as Head Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Jianpeng Cheng, Mirella Lapata, Xingxing Zhang","submitted_at":"2016-06-03T21:27:03Z","abstract_excerpt":"Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call \\textsc{DeNSe} (as shorthand for {\\bf De}pendency {\\bf N}eural {\\bf Se}lection) produces a distribution over possible heads for each word using features obtained from a bidirectional recurrent neural network. Without enforcing structural constraints during training, \\textsc{DeNSe} generates (at inference time) trees for the overwhelming majority o"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1606.01280","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-06-03T21:27:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"70f955571aada7c68c744eb28988ee7ae01730c48ef8c05801080d4a924aab77","abstract_canon_sha256":"058403d931cbc50926c97753db309872840817f2bcb663f9f1d10ef871878fd7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:54:10.713223Z","signature_b64":"Y+pBCho6u0vhhd6hOpblj4RsvYbQNXV1HsrQfleG7Nhyq0NsJIFYpp6Cyj5Wr/hh62ebxJGiqCaiTt7rOz/OBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"591c952313ef71b076407790c8c87acb0e6dd027e370928f7eeeb437fd85d50b","last_reissued_at":"2026-05-18T00:54:10.712817Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:54:10.712817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dependency Parsing as Head Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Jianpeng Cheng, Mirella Lapata, Xingxing Zhang","submitted_at":"2016-06-03T21:27:03Z","abstract_excerpt":"Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call \\textsc{DeNSe} (as shorthand for {\\bf De}pendency {\\bf N}eural {\\bf Se}lection) produces a distribution over possible heads for each word using features obtained from a bidirectional recurrent neural network. Without enforcing structural constraints during training, \\textsc{DeNSe} generates (at inference time) trees for the overwhelming majority o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.01280","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1606.01280","created_at":"2026-05-18T00:54:10.712885+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.01280v4","created_at":"2026-05-18T00:54:10.712885+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.01280","created_at":"2026-05-18T00:54:10.712885+00:00"},{"alias_kind":"pith_short_12","alias_value":"LEOJKIYT55Y3","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_16","alias_value":"LEOJKIYT55Y3A5SA","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_8","alias_value":"LEOJKIYT","created_at":"2026-05-18T12:30:29.479603+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LEOJKIYT55Y3A5SAO6IMRSD2ZM","json":"https://pith.science/pith/LEOJKIYT55Y3A5SAO6IMRSD2ZM.json","graph_json":"https://pith.science/api/pith-number/LEOJKIYT55Y3A5SAO6IMRSD2ZM/graph.json","events_json":"https://pith.science/api/pith-number/LEOJKIYT55Y3A5SAO6IMRSD2ZM/events.json","paper":"https://pith.science/paper/LEOJKIYT"},"agent_actions":{"view_html":"https://pith.science/pith/LEOJKIYT55Y3A5SAO6IMRSD2ZM","download_json":"https://pith.science/pith/LEOJKIYT55Y3A5SAO6IMRSD2ZM.json","view_paper":"https://pith.science/paper/LEOJKIYT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.01280&json=true","fetch_graph":"https://pith.science/api/pith-number/LEOJKIYT55Y3A5SAO6IMRSD2ZM/graph.json","fetch_events":"https://pith.science/api/pith-number/LEOJKIYT55Y3A5SAO6IMRSD2ZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LEOJKIYT55Y3A5SAO6IMRSD2ZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LEOJKIYT55Y3A5SAO6IMRSD2ZM/action/storage_attestation","attest_author":"https://pith.science/pith/LEOJKIYT55Y3A5SAO6IMRSD2ZM/action/author_attestation","sign_citation":"https://pith.science/pith/LEOJKIYT55Y3A5SAO6IMRSD2ZM/action/citation_signature","submit_replication":"https://pith.science/pith/LEOJKIYT55Y3A5SAO6IMRSD2ZM/action/replication_record"}},"created_at":"2026-05-18T00:54:10.712885+00:00","updated_at":"2026-05-18T00:54:10.712885+00:00"}