{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:JYJTAZ2QITBESP4TJE4XDJEZBL","short_pith_number":"pith:JYJTAZ2Q","canonical_record":{"source":{"id":"1407.1687","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"cs.CL","submitted_at":"2014-07-07T12:45:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"08eb797c0bf0ea4dffa7d7449b26d6420e831a474fc8877978db8654cb45ec80","abstract_canon_sha256":"46e5d4bff78ed2784c059d2343c71e2bdc3eeb8472dc2d960aab1b60ecd7d1fa"},"schema_version":"1.0"},"canonical_sha256":"4e1330675044c2493f93493971a4990ac24b3613e90c404d8d8376e21ee041bb","source":{"kind":"arxiv","id":"1407.1687","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1407.1687","created_at":"2026-05-18T02:43:25Z"},{"alias_kind":"arxiv_version","alias_value":"1407.1687v3","created_at":"2026-05-18T02:43:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1407.1687","created_at":"2026-05-18T02:43:25Z"},{"alias_kind":"pith_short_12","alias_value":"JYJTAZ2QITBE","created_at":"2026-05-18T12:28:35Z"},{"alias_kind":"pith_short_16","alias_value":"JYJTAZ2QITBESP4T","created_at":"2026-05-18T12:28:35Z"},{"alias_kind":"pith_short_8","alias_value":"JYJTAZ2Q","created_at":"2026-05-18T12:28:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:JYJTAZ2QITBESP4TJE4XDJEZBL","target":"record","payload":{"canonical_record":{"source":{"id":"1407.1687","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"cs.CL","submitted_at":"2014-07-07T12:45:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"08eb797c0bf0ea4dffa7d7449b26d6420e831a474fc8877978db8654cb45ec80","abstract_canon_sha256":"46e5d4bff78ed2784c059d2343c71e2bdc3eeb8472dc2d960aab1b60ecd7d1fa"},"schema_version":"1.0"},"canonical_sha256":"4e1330675044c2493f93493971a4990ac24b3613e90c404d8d8376e21ee041bb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:43:25.816949Z","signature_b64":"WP3aJ9xtfGgF2+sndYVrrRnqtz2aZHzUmLSKdacyh7UdxfINRdkETVVbigseTr2fe9zS5NlLUfdZeVo6jqBDBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e1330675044c2493f93493971a4990ac24b3613e90c404d8d8376e21ee041bb","last_reissued_at":"2026-05-18T02:43:25.816200Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:43:25.816200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1407.1687","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:43:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5APKi4AGqs1hA7Zsw80GNAZICCJbcI199BbjfyexC7W/KHsHISofro2IjPxRkunPbMx1uGCeLNCC3Ph+ne0ZAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:08:30.160564Z"},"content_sha256":"d94db89d66b0f7f51ee5d906ee13320e7669bbf97cbc199631d55dd4a3cbe4dc","schema_version":"1.0","event_id":"sha256:d94db89d66b0f7f51ee5d906ee13320e7669bbf97cbc199631d55dd4a3cbe4dc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:JYJTAZ2QITBESP4TJE4XDJEZBL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"KNET: A General Framework for Learning Word Embedding using Morphological Knowledge","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Bin Gao, Jiang Bian, Qing Cui, Siyu Qiu, Tie-Yan Liu","submitted_at":"2014-07-07T12:45:10Z","abstract_excerpt":"Neural network techniques are widely applied to obtain high-quality distributed representations of words, i.e., word embeddings, to address text mining, information retrieval, and natural language processing tasks. Recently, efficient methods have been proposed to learn word embeddings from context that captures both semantic and syntactic relationships between words. However, it is challenging to handle unseen words or rare words with insufficient context. In this paper, inspired by the study on word recognition process in cognitive psychology, we propose to take advantage of seemingly less o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1407.1687","kind":"arxiv","version":3},"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":"2026-05-18T02:43:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kOKjFWLCXs4qgC/jte2Lctp9c/ga9AQqjMiDsKpA1zjBYYFoXBVGcrofehUXThU8xq/YQexNjoOsrc8k3PkYCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:08:30.161219Z"},"content_sha256":"fad2a076987bab7a7c0255f23f3f91bd85c1c7a0db3b08c9371874d18c95807d","schema_version":"1.0","event_id":"sha256:fad2a076987bab7a7c0255f23f3f91bd85c1c7a0db3b08c9371874d18c95807d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JYJTAZ2QITBESP4TJE4XDJEZBL/bundle.json","state_url":"https://pith.science/pith/JYJTAZ2QITBESP4TJE4XDJEZBL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JYJTAZ2QITBESP4TJE4XDJEZBL/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":"2026-05-26T16:08:30Z","links":{"resolver":"https://pith.science/pith/JYJTAZ2QITBESP4TJE4XDJEZBL","bundle":"https://pith.science/pith/JYJTAZ2QITBESP4TJE4XDJEZBL/bundle.json","state":"https://pith.science/pith/JYJTAZ2QITBESP4TJE4XDJEZBL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JYJTAZ2QITBESP4TJE4XDJEZBL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:JYJTAZ2QITBESP4TJE4XDJEZBL","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":"46e5d4bff78ed2784c059d2343c71e2bdc3eeb8472dc2d960aab1b60ecd7d1fa","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"cs.CL","submitted_at":"2014-07-07T12:45:10Z","title_canon_sha256":"08eb797c0bf0ea4dffa7d7449b26d6420e831a474fc8877978db8654cb45ec80"},"schema_version":"1.0","source":{"id":"1407.1687","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1407.1687","created_at":"2026-05-18T02:43:25Z"},{"alias_kind":"arxiv_version","alias_value":"1407.1687v3","created_at":"2026-05-18T02:43:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1407.1687","created_at":"2026-05-18T02:43:25Z"},{"alias_kind":"pith_short_12","alias_value":"JYJTAZ2QITBE","created_at":"2026-05-18T12:28:35Z"},{"alias_kind":"pith_short_16","alias_value":"JYJTAZ2QITBESP4T","created_at":"2026-05-18T12:28:35Z"},{"alias_kind":"pith_short_8","alias_value":"JYJTAZ2Q","created_at":"2026-05-18T12:28:35Z"}],"graph_snapshots":[{"event_id":"sha256:fad2a076987bab7a7c0255f23f3f91bd85c1c7a0db3b08c9371874d18c95807d","target":"graph","created_at":"2026-05-18T02:43:25Z","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":"Neural network techniques are widely applied to obtain high-quality distributed representations of words, i.e., word embeddings, to address text mining, information retrieval, and natural language processing tasks. Recently, efficient methods have been proposed to learn word embeddings from context that captures both semantic and syntactic relationships between words. However, it is challenging to handle unseen words or rare words with insufficient context. In this paper, inspired by the study on word recognition process in cognitive psychology, we propose to take advantage of seemingly less o","authors_text":"Bin Gao, Jiang Bian, Qing Cui, Siyu Qiu, Tie-Yan Liu","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"cs.CL","submitted_at":"2014-07-07T12:45:10Z","title":"KNET: A General Framework for Learning Word Embedding using Morphological Knowledge"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1407.1687","kind":"arxiv","version":3},"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:d94db89d66b0f7f51ee5d906ee13320e7669bbf97cbc199631d55dd4a3cbe4dc","target":"record","created_at":"2026-05-18T02:43:25Z","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":"46e5d4bff78ed2784c059d2343c71e2bdc3eeb8472dc2d960aab1b60ecd7d1fa","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"cs.CL","submitted_at":"2014-07-07T12:45:10Z","title_canon_sha256":"08eb797c0bf0ea4dffa7d7449b26d6420e831a474fc8877978db8654cb45ec80"},"schema_version":"1.0","source":{"id":"1407.1687","kind":"arxiv","version":3}},"canonical_sha256":"4e1330675044c2493f93493971a4990ac24b3613e90c404d8d8376e21ee041bb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4e1330675044c2493f93493971a4990ac24b3613e90c404d8d8376e21ee041bb","first_computed_at":"2026-05-18T02:43:25.816200Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:43:25.816200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WP3aJ9xtfGgF2+sndYVrrRnqtz2aZHzUmLSKdacyh7UdxfINRdkETVVbigseTr2fe9zS5NlLUfdZeVo6jqBDBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:43:25.816949Z","signed_message":"canonical_sha256_bytes"},"source_id":"1407.1687","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d94db89d66b0f7f51ee5d906ee13320e7669bbf97cbc199631d55dd4a3cbe4dc","sha256:fad2a076987bab7a7c0255f23f3f91bd85c1c7a0db3b08c9371874d18c95807d"],"state_sha256":"3f873a8ced69f605899e8ccc66ff4758e6298d90db228bcc41eb6fbef8dc8d71"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gTgreV5WDLKnDvKo6txDvL3aIm9Yf0uWL5tzd64SyuV/INkW6CiZO0gS9Ojbo+irDHWlVhMx9gBtwB67x89+Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T16:08:30.164928Z","bundle_sha256":"e19701732ad9af770c524e38292f37591ebb9da47b5953ee51bf721c76037b80"}}