{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:VPWUC3J7ZHCAYVCH5EEUAVEMDN","short_pith_number":"pith:VPWUC3J7","canonical_record":{"source":{"id":"1705.02494","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-05-06T15:11:30Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"40aa7a1b5cf377e00e318683506ddbdf74c86dc2d92d560f1bbcf6f9c53f14d9","abstract_canon_sha256":"f4c91b4600ec67c28857ff3d4d4e7d24ee37b303d8b7c2b22dc462f9096dcbc5"},"schema_version":"1.0"},"canonical_sha256":"abed416d3fc9c40c5447e90940548c1b4830886e8980a4c3c44cbcc8081e78f5","source":{"kind":"arxiv","id":"1705.02494","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.02494","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"arxiv_version","alias_value":"1705.02494v3","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.02494","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"pith_short_12","alias_value":"VPWUC3J7ZHCA","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VPWUC3J7ZHCAYVCH","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VPWUC3J7","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:VPWUC3J7ZHCAYVCH5EEUAVEMDN","target":"record","payload":{"canonical_record":{"source":{"id":"1705.02494","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-05-06T15:11:30Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"40aa7a1b5cf377e00e318683506ddbdf74c86dc2d92d560f1bbcf6f9c53f14d9","abstract_canon_sha256":"f4c91b4600ec67c28857ff3d4d4e7d24ee37b303d8b7c2b22dc462f9096dcbc5"},"schema_version":"1.0"},"canonical_sha256":"abed416d3fc9c40c5447e90940548c1b4830886e8980a4c3c44cbcc8081e78f5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:12.894880Z","signature_b64":"vW0DPAfD1w94SnLl6sMJChU7osy235EB8b9tHZSYLEtHZCSyVx6lz8cn3IfRoYXMnqYxbwa8K9Lu1Bkh41vDDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"abed416d3fc9c40c5447e90940548c1b4830886e8980a4c3c44cbcc8081e78f5","last_reissued_at":"2026-05-18T00:31:12.894224Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:12.894224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.02494","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":"2026-05-18T00:31:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2NtIs+lDg3ZmkV6EJHiln/6Fyp1mVSo8wK9cF3jynbhpKzlV/lpVozjebxb0ceX/S4JpacmZXGVl8SDDYw0/Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T08:40:50.608547Z"},"content_sha256":"97181cca64924a831dae6b4473832657e2b439959d8f48605da60cb6c42f66d8","schema_version":"1.0","event_id":"sha256:97181cca64924a831dae6b4473832657e2b439959d8f48605da60cb6c42f66d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:VPWUC3J7ZHCAYVCH5EEUAVEMDN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Distributed Representations of Texts and Entities from Knowledge Base","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.CL","authors_text":"Hideaki Takeda, Hiroyuki Shindo, Ikuya Yamada, Yoshiyasu Takefuji","submitted_at":"2017-05-06T15:11:30Z","abstract_excerpt":"We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and superv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.02494","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":"2026-05-18T00:31:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6nmXlcGkDdbp7VAAVzCRWBFJY+YevjE44//6S4HJuNZqWXVn0NGVTyARvAjogod76MIgbsaUAjke54rOXLHKCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T08:40:50.608907Z"},"content_sha256":"11b75d5d24ceae386d0dcb3dc1f1c07afc0a9a3463dadbe265f9d89a9326e1c5","schema_version":"1.0","event_id":"sha256:11b75d5d24ceae386d0dcb3dc1f1c07afc0a9a3463dadbe265f9d89a9326e1c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VPWUC3J7ZHCAYVCH5EEUAVEMDN/bundle.json","state_url":"https://pith.science/pith/VPWUC3J7ZHCAYVCH5EEUAVEMDN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VPWUC3J7ZHCAYVCH5EEUAVEMDN/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":"2026-05-30T08:40:50Z","links":{"resolver":"https://pith.science/pith/VPWUC3J7ZHCAYVCH5EEUAVEMDN","bundle":"https://pith.science/pith/VPWUC3J7ZHCAYVCH5EEUAVEMDN/bundle.json","state":"https://pith.science/pith/VPWUC3J7ZHCAYVCH5EEUAVEMDN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VPWUC3J7ZHCAYVCH5EEUAVEMDN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:VPWUC3J7ZHCAYVCH5EEUAVEMDN","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":"f4c91b4600ec67c28857ff3d4d4e7d24ee37b303d8b7c2b22dc462f9096dcbc5","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-05-06T15:11:30Z","title_canon_sha256":"40aa7a1b5cf377e00e318683506ddbdf74c86dc2d92d560f1bbcf6f9c53f14d9"},"schema_version":"1.0","source":{"id":"1705.02494","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.02494","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"arxiv_version","alias_value":"1705.02494v3","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.02494","created_at":"2026-05-18T00:31:12Z"},{"alias_kind":"pith_short_12","alias_value":"VPWUC3J7ZHCA","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VPWUC3J7ZHCAYVCH","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VPWUC3J7","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:11b75d5d24ceae386d0dcb3dc1f1c07afc0a9a3463dadbe265f9d89a9326e1c5","target":"graph","created_at":"2026-05-18T00:31:12Z","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 describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and superv","authors_text":"Hideaki Takeda, Hiroyuki Shindo, Ikuya Yamada, Yoshiyasu Takefuji","cross_cats":["cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-05-06T15:11:30Z","title":"Learning Distributed Representations of Texts and Entities from Knowledge Base"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.02494","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":"sha256:97181cca64924a831dae6b4473832657e2b439959d8f48605da60cb6c42f66d8","target":"record","created_at":"2026-05-18T00:31:12Z","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":"f4c91b4600ec67c28857ff3d4d4e7d24ee37b303d8b7c2b22dc462f9096dcbc5","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-05-06T15:11:30Z","title_canon_sha256":"40aa7a1b5cf377e00e318683506ddbdf74c86dc2d92d560f1bbcf6f9c53f14d9"},"schema_version":"1.0","source":{"id":"1705.02494","kind":"arxiv","version":3}},"canonical_sha256":"abed416d3fc9c40c5447e90940548c1b4830886e8980a4c3c44cbcc8081e78f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"abed416d3fc9c40c5447e90940548c1b4830886e8980a4c3c44cbcc8081e78f5","first_computed_at":"2026-05-18T00:31:12.894224Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:12.894224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vW0DPAfD1w94SnLl6sMJChU7osy235EB8b9tHZSYLEtHZCSyVx6lz8cn3IfRoYXMnqYxbwa8K9Lu1Bkh41vDDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:12.894880Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.02494","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:97181cca64924a831dae6b4473832657e2b439959d8f48605da60cb6c42f66d8","sha256:11b75d5d24ceae386d0dcb3dc1f1c07afc0a9a3463dadbe265f9d89a9326e1c5"],"state_sha256":"e6350b5e9ed8e7df12b144a9f0ceee069acc1fdfeea8bdb7c46874c29bc55b1d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7NYH1CU/d33h771imQT/Bc4/hI6rdMv01Dkm1kd2q3twc4+jONZQ8RUjcYvlQPDln6iEqKzffncugJQccJbUAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T08:40:50.610922Z","bundle_sha256":"7b3e86faa9574be897f8f79702fc5e815940e8b68093f0f9f962ad936d439049"}}