{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:HLXWJKH77Q4YRD5KYGZDAVQ5I2","short_pith_number":"pith:HLXWJKH7","canonical_record":{"source":{"id":"1612.02703","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-12-08T15:54:00Z","cross_cats_sorted":[],"title_canon_sha256":"f99f2dba7d0e0e58cc383b37285e0189afc4c2a5a67982fce6c60e8b2d6f87af","abstract_canon_sha256":"4b0b3956e736953e00d616314e40193427d37a8788ef324792cadc8a57ddf247"},"schema_version":"1.0"},"canonical_sha256":"3aef64a8fffc39888faac1b230561d4692d12e9065643cabed049b399969f7d8","source":{"kind":"arxiv","id":"1612.02703","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.02703","created_at":"2026-05-18T00:41:58Z"},{"alias_kind":"arxiv_version","alias_value":"1612.02703v2","created_at":"2026-05-18T00:41:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02703","created_at":"2026-05-18T00:41:58Z"},{"alias_kind":"pith_short_12","alias_value":"HLXWJKH77Q4Y","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"HLXWJKH77Q4YRD5K","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"HLXWJKH7","created_at":"2026-05-18T12:30:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:HLXWJKH77Q4YRD5KYGZDAVQ5I2","target":"record","payload":{"canonical_record":{"source":{"id":"1612.02703","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-12-08T15:54:00Z","cross_cats_sorted":[],"title_canon_sha256":"f99f2dba7d0e0e58cc383b37285e0189afc4c2a5a67982fce6c60e8b2d6f87af","abstract_canon_sha256":"4b0b3956e736953e00d616314e40193427d37a8788ef324792cadc8a57ddf247"},"schema_version":"1.0"},"canonical_sha256":"3aef64a8fffc39888faac1b230561d4692d12e9065643cabed049b399969f7d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:58.033431Z","signature_b64":"RX7oZHxSntMOxnUCcQBFMWV3dFFRUUoySDVyChtpd2qcXmaNpG4jh+3UpMoY8SWCx+Ykpx3xGT4XphKRzTrTAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3aef64a8fffc39888faac1b230561d4692d12e9065643cabed049b399969f7d8","last_reissued_at":"2026-05-18T00:41:58.032808Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:58.032808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.02703","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:41:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MoJ1vdw5Cgh3yn9rwxqMT6pnNPGnfbY/eNwZc3enCuG9NKPRYZvvVRscV3W52w0jCLFeSvs4HXqZUUngTeyoDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T04:41:58.603075Z"},"content_sha256":"1a207352f74f630a1eaac91663810d03caed8ebc86ad6e750daa34ff2d2a15bf","schema_version":"1.0","event_id":"sha256:1a207352f74f630a1eaac91663810d03caed8ebc86ad6e750daa34ff2d2a15bf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:HLXWJKH77Q4YRD5KYGZDAVQ5I2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Embedding Words and Senses Together via Joint Knowledge-Enhanced Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ignacio Iacobacci, Jose Camacho-Collados, Massimiliano Mancini, Roberto Navigli","submitted_at":"2016-12-08T15:54:00Z","abstract_excerpt":"Word embeddings are widely used in Natural Language Processing, mainly due to their success in capturing semantic information from massive corpora. However, their creation process does not allow the different meanings of a word to be automatically separated, as it conflates them into a single vector. We address this issue by proposing a new model which learns word and sense embeddings jointly. Our model exploits large corpora and knowledge from semantic networks in order to produce a unified vector space of word and sense embeddings. We evaluate the main features of our approach both qualitati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02703","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:41:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gFY45V93ucnrA8Cb81lqOi4bi+II61XucGINQMZfV4SyDVaCywPu40fDOJjU4QwzHA2q9sOAoSiwRCF9PNxODw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T04:41:58.603433Z"},"content_sha256":"223a84c10c0bfced3f6aaec2b94eac89d4778c89e5246e03019109dc78c6256f","schema_version":"1.0","event_id":"sha256:223a84c10c0bfced3f6aaec2b94eac89d4778c89e5246e03019109dc78c6256f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HLXWJKH77Q4YRD5KYGZDAVQ5I2/bundle.json","state_url":"https://pith.science/pith/HLXWJKH77Q4YRD5KYGZDAVQ5I2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HLXWJKH77Q4YRD5KYGZDAVQ5I2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-08T04:41:58Z","links":{"resolver":"https://pith.science/pith/HLXWJKH77Q4YRD5KYGZDAVQ5I2","bundle":"https://pith.science/pith/HLXWJKH77Q4YRD5KYGZDAVQ5I2/bundle.json","state":"https://pith.science/pith/HLXWJKH77Q4YRD5KYGZDAVQ5I2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HLXWJKH77Q4YRD5KYGZDAVQ5I2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:HLXWJKH77Q4YRD5KYGZDAVQ5I2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4b0b3956e736953e00d616314e40193427d37a8788ef324792cadc8a57ddf247","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-12-08T15:54:00Z","title_canon_sha256":"f99f2dba7d0e0e58cc383b37285e0189afc4c2a5a67982fce6c60e8b2d6f87af"},"schema_version":"1.0","source":{"id":"1612.02703","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.02703","created_at":"2026-05-18T00:41:58Z"},{"alias_kind":"arxiv_version","alias_value":"1612.02703v2","created_at":"2026-05-18T00:41:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02703","created_at":"2026-05-18T00:41:58Z"},{"alias_kind":"pith_short_12","alias_value":"HLXWJKH77Q4Y","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"HLXWJKH77Q4YRD5K","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"HLXWJKH7","created_at":"2026-05-18T12:30:19Z"}],"graph_snapshots":[{"event_id":"sha256:223a84c10c0bfced3f6aaec2b94eac89d4778c89e5246e03019109dc78c6256f","target":"graph","created_at":"2026-05-18T00:41:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Word embeddings are widely used in Natural Language Processing, mainly due to their success in capturing semantic information from massive corpora. However, their creation process does not allow the different meanings of a word to be automatically separated, as it conflates them into a single vector. We address this issue by proposing a new model which learns word and sense embeddings jointly. Our model exploits large corpora and knowledge from semantic networks in order to produce a unified vector space of word and sense embeddings. We evaluate the main features of our approach both qualitati","authors_text":"Ignacio Iacobacci, Jose Camacho-Collados, Massimiliano Mancini, Roberto Navigli","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-12-08T15:54:00Z","title":"Embedding Words and Senses Together via Joint Knowledge-Enhanced Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02703","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1a207352f74f630a1eaac91663810d03caed8ebc86ad6e750daa34ff2d2a15bf","target":"record","created_at":"2026-05-18T00:41:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4b0b3956e736953e00d616314e40193427d37a8788ef324792cadc8a57ddf247","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-12-08T15:54:00Z","title_canon_sha256":"f99f2dba7d0e0e58cc383b37285e0189afc4c2a5a67982fce6c60e8b2d6f87af"},"schema_version":"1.0","source":{"id":"1612.02703","kind":"arxiv","version":2}},"canonical_sha256":"3aef64a8fffc39888faac1b230561d4692d12e9065643cabed049b399969f7d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3aef64a8fffc39888faac1b230561d4692d12e9065643cabed049b399969f7d8","first_computed_at":"2026-05-18T00:41:58.032808Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:58.032808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RX7oZHxSntMOxnUCcQBFMWV3dFFRUUoySDVyChtpd2qcXmaNpG4jh+3UpMoY8SWCx+Ykpx3xGT4XphKRzTrTAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:58.033431Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.02703","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1a207352f74f630a1eaac91663810d03caed8ebc86ad6e750daa34ff2d2a15bf","sha256:223a84c10c0bfced3f6aaec2b94eac89d4778c89e5246e03019109dc78c6256f"],"state_sha256":"401fb3ab351cab1c402d007744ee0821b6f77816fe44885adaeb0b8dcbd7430f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FGBnGzlA4Xp7hy/rqtHkugr+4t8e9CKaRRiOP0g1qalVd1ceG2ruAQLwmPDg/vNSuTT83Zdy4VSYMFxAu+crAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T04:41:58.605393Z","bundle_sha256":"b482b97d7df166e54de7a9eb806470a74557a11a670522d451f5b1e962e5fb3e"}}