{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:FNBLOU6T5NZ5FN4KTWO4F3IOQ5","short_pith_number":"pith:FNBLOU6T","canonical_record":{"source":{"id":"1708.04755","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2017-08-16T03:17:57Z","cross_cats_sorted":[],"title_canon_sha256":"9fc9720ed3dfa796e4a3c4c9069702a378eacfc0983f92f82b9ccb74792117c0","abstract_canon_sha256":"c7d8701d490b569bc55f5dfeae26e2b94ea520a0b481007d930a449fb8e7209e"},"schema_version":"1.0"},"canonical_sha256":"2b42b753d3eb73d2b78a9d9dc2ed0e876faf83429fc852fe04580858372ebb4a","source":{"kind":"arxiv","id":"1708.04755","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.04755","created_at":"2026-05-18T00:37:57Z"},{"alias_kind":"arxiv_version","alias_value":"1708.04755v1","created_at":"2026-05-18T00:37:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04755","created_at":"2026-05-18T00:37:57Z"},{"alias_kind":"pith_short_12","alias_value":"FNBLOU6T5NZ5","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FNBLOU6T5NZ5FN4K","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FNBLOU6T","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:FNBLOU6T5NZ5FN4KTWO4F3IOQ5","target":"record","payload":{"canonical_record":{"source":{"id":"1708.04755","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2017-08-16T03:17:57Z","cross_cats_sorted":[],"title_canon_sha256":"9fc9720ed3dfa796e4a3c4c9069702a378eacfc0983f92f82b9ccb74792117c0","abstract_canon_sha256":"c7d8701d490b569bc55f5dfeae26e2b94ea520a0b481007d930a449fb8e7209e"},"schema_version":"1.0"},"canonical_sha256":"2b42b753d3eb73d2b78a9d9dc2ed0e876faf83429fc852fe04580858372ebb4a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:37:57.993165Z","signature_b64":"expR5vwBOdVGKXk3bVyPe04uJN31HCatZNmr/Xy8HW+cKFLqJ1V6L22pHTqL3bW/A2nZDf6d4B8CiCIeoibsCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b42b753d3eb73d2b78a9d9dc2ed0e876faf83429fc852fe04580858372ebb4a","last_reissued_at":"2026-05-18T00:37:57.992636Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:37:57.992636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.04755","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":"2026-05-18T00:37:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dHdUiPpJJGWndeVNB5D4K8Pd5qqMz2MqZItXlK3OtKEkKe2GbfKMcW/CUcrRa67AF5y1R8XBWxlPK5q/9WYNDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T14:55:47.380618Z"},"content_sha256":"9120dbe6725adbe09c54a579d063220d1efc5eee90082e5347084c8370d50937","schema_version":"1.0","event_id":"sha256:9120dbe6725adbe09c54a579d063220d1efc5eee90082e5347084c8370d50937"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:FNBLOU6T5NZ5FN4KTWO4F3IOQ5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Chinese Word Representations From Glyphs Of Characters","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hung-yi Lee, Tzu-Ray Su","submitted_at":"2017-08-16T03:17:57Z","abstract_excerpt":"In this paper, we propose new methods to learn Chinese word representations. Chinese characters are composed of graphical components, which carry rich semantics. It is common for a Chinese learner to comprehend the meaning of a word from these graphical components. As a result, we propose models that enhance word representations by character glyphs. The character glyph features are directly learned from the bitmaps of characters by convolutional auto-encoder(convAE), and the glyph features improve Chinese word representations which are already enhanced by character embeddings. Another contribu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04755","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":"2026-05-18T00:37:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2nEYQ269AMhNaTbSm4NoUu5Ff/LucZ+YpLJqt0XA91MCapaEfWcWSgdSYTji85D6p2Rxz54j4vEyG9Zi1+5tAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T14:55:47.381150Z"},"content_sha256":"3b1ba9d4cb0623e5ccfef06a6f80652bfc60416527d132799bbafcbd0c9b48c8","schema_version":"1.0","event_id":"sha256:3b1ba9d4cb0623e5ccfef06a6f80652bfc60416527d132799bbafcbd0c9b48c8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FNBLOU6T5NZ5FN4KTWO4F3IOQ5/bundle.json","state_url":"https://pith.science/pith/FNBLOU6T5NZ5FN4KTWO4F3IOQ5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FNBLOU6T5NZ5FN4KTWO4F3IOQ5/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":"2026-08-08T14:55:47Z","links":{"resolver":"https://pith.science/pith/FNBLOU6T5NZ5FN4KTWO4F3IOQ5","bundle":"https://pith.science/pith/FNBLOU6T5NZ5FN4KTWO4F3IOQ5/bundle.json","state":"https://pith.science/pith/FNBLOU6T5NZ5FN4KTWO4F3IOQ5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FNBLOU6T5NZ5FN4KTWO4F3IOQ5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:FNBLOU6T5NZ5FN4KTWO4F3IOQ5","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":"c7d8701d490b569bc55f5dfeae26e2b94ea520a0b481007d930a449fb8e7209e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2017-08-16T03:17:57Z","title_canon_sha256":"9fc9720ed3dfa796e4a3c4c9069702a378eacfc0983f92f82b9ccb74792117c0"},"schema_version":"1.0","source":{"id":"1708.04755","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.04755","created_at":"2026-05-18T00:37:57Z"},{"alias_kind":"arxiv_version","alias_value":"1708.04755v1","created_at":"2026-05-18T00:37:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04755","created_at":"2026-05-18T00:37:57Z"},{"alias_kind":"pith_short_12","alias_value":"FNBLOU6T5NZ5","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FNBLOU6T5NZ5FN4K","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FNBLOU6T","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:3b1ba9d4cb0623e5ccfef06a6f80652bfc60416527d132799bbafcbd0c9b48c8","target":"graph","created_at":"2026-05-18T00:37:57Z","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":"In this paper, we propose new methods to learn Chinese word representations. Chinese characters are composed of graphical components, which carry rich semantics. It is common for a Chinese learner to comprehend the meaning of a word from these graphical components. As a result, we propose models that enhance word representations by character glyphs. The character glyph features are directly learned from the bitmaps of characters by convolutional auto-encoder(convAE), and the glyph features improve Chinese word representations which are already enhanced by character embeddings. Another contribu","authors_text":"Hung-yi Lee, Tzu-Ray Su","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2017-08-16T03:17:57Z","title":"Learning Chinese Word Representations From Glyphs Of Characters"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04755","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":"sha256:9120dbe6725adbe09c54a579d063220d1efc5eee90082e5347084c8370d50937","target":"record","created_at":"2026-05-18T00:37:57Z","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":"c7d8701d490b569bc55f5dfeae26e2b94ea520a0b481007d930a449fb8e7209e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2017-08-16T03:17:57Z","title_canon_sha256":"9fc9720ed3dfa796e4a3c4c9069702a378eacfc0983f92f82b9ccb74792117c0"},"schema_version":"1.0","source":{"id":"1708.04755","kind":"arxiv","version":1}},"canonical_sha256":"2b42b753d3eb73d2b78a9d9dc2ed0e876faf83429fc852fe04580858372ebb4a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2b42b753d3eb73d2b78a9d9dc2ed0e876faf83429fc852fe04580858372ebb4a","first_computed_at":"2026-05-18T00:37:57.992636Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:37:57.992636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"expR5vwBOdVGKXk3bVyPe04uJN31HCatZNmr/Xy8HW+cKFLqJ1V6L22pHTqL3bW/A2nZDf6d4B8CiCIeoibsCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:37:57.993165Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.04755","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9120dbe6725adbe09c54a579d063220d1efc5eee90082e5347084c8370d50937","sha256:3b1ba9d4cb0623e5ccfef06a6f80652bfc60416527d132799bbafcbd0c9b48c8"],"state_sha256":"3f5dc5962d3ddee5c29742c518fc22612a484beafce3f62e29365465da6b40de"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cQGGT2SeXCC59JnSiPRD6urEgLv6ssybD/mzlkQNoB24KCmlUzmGXxUNSGMt70J1bGHU7C2zAU+DKkXb7gAKBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T14:55:47.386211Z","bundle_sha256":"b0d745b8ed89bd34bd28ce3a7831d87cb0cb897fd43e5090850b2b734112fa23"}}