{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:IMDUFEAPOQPTN4L4XIXMUUNQEN","short_pith_number":"pith:IMDUFEAP","canonical_record":{"source":{"id":"1608.05852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-20T17:34:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9db1ff75abdc83be94041b2f377efb49338a6c98be4759a978d79bcb7204c746","abstract_canon_sha256":"0bc534cc1a224fcfac12a6859732006d1dac90f7713dd7dafeeb688baba680a8"},"schema_version":"1.0"},"canonical_sha256":"430742900f741f36f17cba2eca51b02371968301cdd1552291d58d0b912a43e1","source":{"kind":"arxiv","id":"1608.05852","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.05852","created_at":"2026-05-18T01:08:22Z"},{"alias_kind":"arxiv_version","alias_value":"1608.05852v1","created_at":"2026-05-18T01:08:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.05852","created_at":"2026-05-18T01:08:22Z"},{"alias_kind":"pith_short_12","alias_value":"IMDUFEAPOQPT","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"IMDUFEAPOQPTN4L4","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"IMDUFEAP","created_at":"2026-05-18T12:30:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:IMDUFEAPOQPTN4L4XIXMUUNQEN","target":"record","payload":{"canonical_record":{"source":{"id":"1608.05852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-20T17:34:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9db1ff75abdc83be94041b2f377efb49338a6c98be4759a978d79bcb7204c746","abstract_canon_sha256":"0bc534cc1a224fcfac12a6859732006d1dac90f7713dd7dafeeb688baba680a8"},"schema_version":"1.0"},"canonical_sha256":"430742900f741f36f17cba2eca51b02371968301cdd1552291d58d0b912a43e1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:08:22.984749Z","signature_b64":"7vYsWmWv4djD8hs0ZyMYo9NBzn+0pmIIZ3yA8g9rqSgyPuWIu/h58JSh/hGTWxRsdmJ9s2sJ/sbJvTfkpjB8Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"430742900f741f36f17cba2eca51b02371968301cdd1552291d58d0b912a43e1","last_reissued_at":"2026-05-18T01:08:22.984065Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:08:22.984065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.05852","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:08:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uoyMr1+jLFRZFoVjVsOL6jAjmd9bUUEHVz3n+5P7KPqXMsADglaQHnSitiS58XEIalc5cb93+wMh8CRfG0P+CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T09:53:27.789408Z"},"content_sha256":"c8cfec033b8064d5d29930b5be45f9b9a8b57efeece467bec263707227d3d25d","schema_version":"1.0","event_id":"sha256:c8cfec033b8064d5d29930b5be45f9b9a8b57efeece467bec263707227d3d25d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:IMDUFEAPOQPTN4L4XIXMUUNQEN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Word Embeddings from Intrinsic and Extrinsic Views","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jifan Chen, Kan Chen, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zheng Zhang","submitted_at":"2016-08-20T17:34:38Z","abstract_excerpt":"While word embeddings are currently predominant for natural language processing, most of existing models learn them solely from their contexts. However, these context-based word embeddings are limited since not all words' meaning can be learned based on only context. Moreover, it is also difficult to learn the representation of the rare words due to data sparsity problem. In this work, we address these issues by learning the representations of words by integrating their intrinsic (descriptive) and extrinsic (contextual) information. To prove the effectiveness of our model, we evaluate it on fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.05852","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:08:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kam7weGjsal+amRfj4WLYb4Hl5OgAlEFJm6pT6OA3Hvp0OukkCqBnXqB98Ph4NTA1xQHixQU/jue6ZenXeEvDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T09:53:27.790084Z"},"content_sha256":"ff284a6fc371db5d578232364c52ef47f59dd621f5aca829b6e1dca3b469d02f","schema_version":"1.0","event_id":"sha256:ff284a6fc371db5d578232364c52ef47f59dd621f5aca829b6e1dca3b469d02f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IMDUFEAPOQPTN4L4XIXMUUNQEN/bundle.json","state_url":"https://pith.science/pith/IMDUFEAPOQPTN4L4XIXMUUNQEN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IMDUFEAPOQPTN4L4XIXMUUNQEN/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":"2026-05-23T09:53:27Z","links":{"resolver":"https://pith.science/pith/IMDUFEAPOQPTN4L4XIXMUUNQEN","bundle":"https://pith.science/pith/IMDUFEAPOQPTN4L4XIXMUUNQEN/bundle.json","state":"https://pith.science/pith/IMDUFEAPOQPTN4L4XIXMUUNQEN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IMDUFEAPOQPTN4L4XIXMUUNQEN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:IMDUFEAPOQPTN4L4XIXMUUNQEN","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":"0bc534cc1a224fcfac12a6859732006d1dac90f7713dd7dafeeb688baba680a8","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-20T17:34:38Z","title_canon_sha256":"9db1ff75abdc83be94041b2f377efb49338a6c98be4759a978d79bcb7204c746"},"schema_version":"1.0","source":{"id":"1608.05852","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.05852","created_at":"2026-05-18T01:08:22Z"},{"alias_kind":"arxiv_version","alias_value":"1608.05852v1","created_at":"2026-05-18T01:08:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.05852","created_at":"2026-05-18T01:08:22Z"},{"alias_kind":"pith_short_12","alias_value":"IMDUFEAPOQPT","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"IMDUFEAPOQPTN4L4","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"IMDUFEAP","created_at":"2026-05-18T12:30:22Z"}],"graph_snapshots":[{"event_id":"sha256:ff284a6fc371db5d578232364c52ef47f59dd621f5aca829b6e1dca3b469d02f","target":"graph","created_at":"2026-05-18T01:08:22Z","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":"While word embeddings are currently predominant for natural language processing, most of existing models learn them solely from their contexts. However, these context-based word embeddings are limited since not all words' meaning can be learned based on only context. Moreover, it is also difficult to learn the representation of the rare words due to data sparsity problem. In this work, we address these issues by learning the representations of words by integrating their intrinsic (descriptive) and extrinsic (contextual) information. To prove the effectiveness of our model, we evaluate it on fo","authors_text":"Jifan Chen, Kan Chen, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zheng Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-20T17:34:38Z","title":"Learning Word Embeddings from Intrinsic and Extrinsic Views"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.05852","kind":"arxiv","version":1},"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:c8cfec033b8064d5d29930b5be45f9b9a8b57efeece467bec263707227d3d25d","target":"record","created_at":"2026-05-18T01:08:22Z","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":"0bc534cc1a224fcfac12a6859732006d1dac90f7713dd7dafeeb688baba680a8","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-20T17:34:38Z","title_canon_sha256":"9db1ff75abdc83be94041b2f377efb49338a6c98be4759a978d79bcb7204c746"},"schema_version":"1.0","source":{"id":"1608.05852","kind":"arxiv","version":1}},"canonical_sha256":"430742900f741f36f17cba2eca51b02371968301cdd1552291d58d0b912a43e1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"430742900f741f36f17cba2eca51b02371968301cdd1552291d58d0b912a43e1","first_computed_at":"2026-05-18T01:08:22.984065Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:08:22.984065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7vYsWmWv4djD8hs0ZyMYo9NBzn+0pmIIZ3yA8g9rqSgyPuWIu/h58JSh/hGTWxRsdmJ9s2sJ/sbJvTfkpjB8Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:08:22.984749Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.05852","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c8cfec033b8064d5d29930b5be45f9b9a8b57efeece467bec263707227d3d25d","sha256:ff284a6fc371db5d578232364c52ef47f59dd621f5aca829b6e1dca3b469d02f"],"state_sha256":"1a66f532e396d06ae85a5082d1242c7b528266ff1769c475c3f4cca4335557ed"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gWu9pnL/IpbQTY6G8oScXiNyqQRLtMuhrA/CP0Imvee6mwhwsV2vAsR1IijKjK3aDrN4NmIxz0SKEu/hwSp/Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T09:53:27.793503Z","bundle_sha256":"f6f180f0d7e70997b0b1d6721608f812261397169aefe23934d3ac3f635d44b9"}}