{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PR4OIS3A6OC5IMC7H6C36N2HJU","short_pith_number":"pith:PR4OIS3A","schema_version":"1.0","canonical_sha256":"7c78e44b60f385d4305f3f85bf37474d2959db4f6641756048b7dc5f8d3eae15","source":{"kind":"arxiv","id":"1802.02116","version":1},"attestation_state":"computed","paper":{"title":"Non-Projective Dependency Parsing via Latent Heads Representation (LHR)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Matteo Grella, Simone Cangialosi","submitted_at":"2018-02-06T18:28:45Z","abstract_excerpt":"In this paper, we introduce a novel approach based on a bidirectional recurrent autoencoder to perform globally optimized non-projective dependency parsing via semi-supervised learning. The syntactic analysis is completed at the end of the neural process that generates a Latent Heads Representation (LHR), without any algorithmic constraint and with a linear complexity. The resulting \"latent syntactic structure\" can be used directly in other semantic tasks. The LHR is transformed into the usual dependency tree computing a simple vectors similarity. We believe that our model has the potential to"},"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":"1802.02116","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-02-06T18:28:45Z","cross_cats_sorted":[],"title_canon_sha256":"1ef44fbc3bbc5af919722b1716d0871f02c073831946946127076d0f2aaae393","abstract_canon_sha256":"15b6b3bd70c4b05691bfaace32e63fbce477af5f22cc048632a53e92576bb7f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:10.844689Z","signature_b64":"Vxibsh0jIpKypvNPXohXH0dEqYawH52OOpASvuAKpAsB2l4uhUziTbKCHj1ASPq4RNGNBs/AloyPkdeli3A+CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c78e44b60f385d4305f3f85bf37474d2959db4f6641756048b7dc5f8d3eae15","last_reissued_at":"2026-05-18T00:24:10.843882Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:10.843882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Non-Projective Dependency Parsing via Latent Heads Representation (LHR)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Matteo Grella, Simone Cangialosi","submitted_at":"2018-02-06T18:28:45Z","abstract_excerpt":"In this paper, we introduce a novel approach based on a bidirectional recurrent autoencoder to perform globally optimized non-projective dependency parsing via semi-supervised learning. The syntactic analysis is completed at the end of the neural process that generates a Latent Heads Representation (LHR), without any algorithmic constraint and with a linear complexity. The resulting \"latent syntactic structure\" can be used directly in other semantic tasks. The LHR is transformed into the usual dependency tree computing a simple vectors similarity. We believe that our model has the potential to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02116","kind":"arxiv","version":1},"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":"1802.02116","created_at":"2026-05-18T00:24:10.843972+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.02116v1","created_at":"2026-05-18T00:24:10.843972+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02116","created_at":"2026-05-18T00:24:10.843972+00:00"},{"alias_kind":"pith_short_12","alias_value":"PR4OIS3A6OC5","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"PR4OIS3A6OC5IMC7","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"PR4OIS3A","created_at":"2026-05-18T12:32:46.962924+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/PR4OIS3A6OC5IMC7H6C36N2HJU","json":"https://pith.science/pith/PR4OIS3A6OC5IMC7H6C36N2HJU.json","graph_json":"https://pith.science/api/pith-number/PR4OIS3A6OC5IMC7H6C36N2HJU/graph.json","events_json":"https://pith.science/api/pith-number/PR4OIS3A6OC5IMC7H6C36N2HJU/events.json","paper":"https://pith.science/paper/PR4OIS3A"},"agent_actions":{"view_html":"https://pith.science/pith/PR4OIS3A6OC5IMC7H6C36N2HJU","download_json":"https://pith.science/pith/PR4OIS3A6OC5IMC7H6C36N2HJU.json","view_paper":"https://pith.science/paper/PR4OIS3A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.02116&json=true","fetch_graph":"https://pith.science/api/pith-number/PR4OIS3A6OC5IMC7H6C36N2HJU/graph.json","fetch_events":"https://pith.science/api/pith-number/PR4OIS3A6OC5IMC7H6C36N2HJU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PR4OIS3A6OC5IMC7H6C36N2HJU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PR4OIS3A6OC5IMC7H6C36N2HJU/action/storage_attestation","attest_author":"https://pith.science/pith/PR4OIS3A6OC5IMC7H6C36N2HJU/action/author_attestation","sign_citation":"https://pith.science/pith/PR4OIS3A6OC5IMC7H6C36N2HJU/action/citation_signature","submit_replication":"https://pith.science/pith/PR4OIS3A6OC5IMC7H6C36N2HJU/action/replication_record"}},"created_at":"2026-05-18T00:24:10.843972+00:00","updated_at":"2026-05-18T00:24:10.843972+00:00"}