{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:YZRBAIKW3PN6BHHULMYHLJEUKV","short_pith_number":"pith:YZRBAIKW","canonical_record":{"source":{"id":"1710.06371","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2017-10-17T16:39:09Z","cross_cats_sorted":[],"title_canon_sha256":"aa029a3673e1acaedbac67f45ac9049f58bb8bee0db92f151eea9c1862e77c4e","abstract_canon_sha256":"51877316d15f9fed5282b989dd928476942e0a4885c4dcff87e32429e337303f"},"schema_version":"1.0"},"canonical_sha256":"c662102156dbdbe09cf45b3075a494554a42801e3e9ec86428c941060f9d4a83","source":{"kind":"arxiv","id":"1710.06371","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.06371","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"arxiv_version","alias_value":"1710.06371v2","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.06371","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"pith_short_12","alias_value":"YZRBAIKW3PN6","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"YZRBAIKW3PN6BHHU","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"YZRBAIKW","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:YZRBAIKW3PN6BHHULMYHLJEUKV","target":"record","payload":{"canonical_record":{"source":{"id":"1710.06371","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2017-10-17T16:39:09Z","cross_cats_sorted":[],"title_canon_sha256":"aa029a3673e1acaedbac67f45ac9049f58bb8bee0db92f151eea9c1862e77c4e","abstract_canon_sha256":"51877316d15f9fed5282b989dd928476942e0a4885c4dcff87e32429e337303f"},"schema_version":"1.0"},"canonical_sha256":"c662102156dbdbe09cf45b3075a494554a42801e3e9ec86428c941060f9d4a83","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:03.997128Z","signature_b64":"iJPn/pt8Yx6pHdATwRLFg84ymX9z+O7RmerulEv6KlclFF9Xu7Usfu1hBmWaMJSSy9RzWH7mkrG4pPDFgIT0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c662102156dbdbe09cf45b3075a494554a42801e3e9ec86428c941060f9d4a83","last_reissued_at":"2026-05-18T00:18:03.996641Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:03.996641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.06371","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":"2026-05-18T00:18:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NQlfWCCDZApxYJ/nWDSt8tISD4bzMfKBZe2lJmg9OfOTD+2kGkjgWjkoKgqPqd0xY78C9dNa7WSYRkBoJiYeCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T12:14:29.873325Z"},"content_sha256":"ef7a006efe7c0cbc1ab81aa0840963a09bfdff23e596d66c3373f2701d21eacf","schema_version":"1.0","event_id":"sha256:ef7a006efe7c0cbc1ab81aa0840963a09bfdff23e596d66c3373f2701d21eacf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:YZRBAIKW3PN6BHHULMYHLJEUKV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Specialising Word Vectors for Lexical Entailment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ivan Vuli\\'c, Nikola Mrk\\v{s}i\\'c","submitted_at":"2017-10-17T16:39:09Z","abstract_excerpt":"We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy-hypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNet-style hierarchy of concepts. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.06371","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":"2026-05-18T00:18:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TiDdIpl6AhXDpZd4NPb949tolMvy9AdGV9UbQpegEbkaY4F3uGLV1T6Ai5Tqycf6hGebU36dpcjdsQAiaDayCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T12:14:29.873676Z"},"content_sha256":"e25c7865b581a68817a5bbe2ca81f0244c024e9a53d48aa0b794c667f30cce64","schema_version":"1.0","event_id":"sha256:e25c7865b581a68817a5bbe2ca81f0244c024e9a53d48aa0b794c667f30cce64"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YZRBAIKW3PN6BHHULMYHLJEUKV/bundle.json","state_url":"https://pith.science/pith/YZRBAIKW3PN6BHHULMYHLJEUKV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YZRBAIKW3PN6BHHULMYHLJEUKV/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":"2026-06-09T12:14:29Z","links":{"resolver":"https://pith.science/pith/YZRBAIKW3PN6BHHULMYHLJEUKV","bundle":"https://pith.science/pith/YZRBAIKW3PN6BHHULMYHLJEUKV/bundle.json","state":"https://pith.science/pith/YZRBAIKW3PN6BHHULMYHLJEUKV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YZRBAIKW3PN6BHHULMYHLJEUKV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:YZRBAIKW3PN6BHHULMYHLJEUKV","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":"51877316d15f9fed5282b989dd928476942e0a4885c4dcff87e32429e337303f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2017-10-17T16:39:09Z","title_canon_sha256":"aa029a3673e1acaedbac67f45ac9049f58bb8bee0db92f151eea9c1862e77c4e"},"schema_version":"1.0","source":{"id":"1710.06371","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.06371","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"arxiv_version","alias_value":"1710.06371v2","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.06371","created_at":"2026-05-18T00:18:03Z"},{"alias_kind":"pith_short_12","alias_value":"YZRBAIKW3PN6","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"YZRBAIKW3PN6BHHU","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"YZRBAIKW","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:e25c7865b581a68817a5bbe2ca81f0244c024e9a53d48aa0b794c667f30cce64","target":"graph","created_at":"2026-05-18T00:18:03Z","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":"We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy-hypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNet-style hierarchy of concepts. ","authors_text":"Ivan Vuli\\'c, Nikola Mrk\\v{s}i\\'c","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2017-10-17T16:39:09Z","title":"Specialising Word Vectors for Lexical Entailment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.06371","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:ef7a006efe7c0cbc1ab81aa0840963a09bfdff23e596d66c3373f2701d21eacf","target":"record","created_at":"2026-05-18T00:18:03Z","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":"51877316d15f9fed5282b989dd928476942e0a4885c4dcff87e32429e337303f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2017-10-17T16:39:09Z","title_canon_sha256":"aa029a3673e1acaedbac67f45ac9049f58bb8bee0db92f151eea9c1862e77c4e"},"schema_version":"1.0","source":{"id":"1710.06371","kind":"arxiv","version":2}},"canonical_sha256":"c662102156dbdbe09cf45b3075a494554a42801e3e9ec86428c941060f9d4a83","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c662102156dbdbe09cf45b3075a494554a42801e3e9ec86428c941060f9d4a83","first_computed_at":"2026-05-18T00:18:03.996641Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:18:03.996641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iJPn/pt8Yx6pHdATwRLFg84ymX9z+O7RmerulEv6KlclFF9Xu7Usfu1hBmWaMJSSy9RzWH7mkrG4pPDFgIT0DA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:18:03.997128Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.06371","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ef7a006efe7c0cbc1ab81aa0840963a09bfdff23e596d66c3373f2701d21eacf","sha256:e25c7865b581a68817a5bbe2ca81f0244c024e9a53d48aa0b794c667f30cce64"],"state_sha256":"5c0267c1391327181e201674996192f86c8c04c4223e71cceeab3885ec8334fb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xk041UFn2/dmb53rXRstqwnktMUIpuzf5cuCjVvgLRMljtLLREeV5P5sf2XCx6Uu0jvMRoHH5J/S1HpfszpMAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T12:14:29.875590Z","bundle_sha256":"2255a937699ddd51069816ce1b2f03eb8a4acdff1f1fcb32b994e0ea77101782"}}