{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:C52GSJWTPDGYNVHKJHSFRAUG4Y","short_pith_number":"pith:C52GSJWT","canonical_record":{"source":{"id":"1805.10274","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2018-05-25T17:48:20Z","cross_cats_sorted":[],"title_canon_sha256":"f6fbf7e23f7e96381c0d54092210ddd9477409ce01c66082ab78c6fdeae84acc","abstract_canon_sha256":"091c499f553ef64d840914162ed32862b013da22512c393242b89ca4de058ac5"},"schema_version":"1.0"},"canonical_sha256":"17746926d378cd86d4ea49e4588286e615207a4be62d56fd6ec154749564e837","source":{"kind":"arxiv","id":"1805.10274","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.10274","created_at":"2026-05-18T00:14:57Z"},{"alias_kind":"arxiv_version","alias_value":"1805.10274v1","created_at":"2026-05-18T00:14:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10274","created_at":"2026-05-18T00:14:57Z"},{"alias_kind":"pith_short_12","alias_value":"C52GSJWTPDGY","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"C52GSJWTPDGYNVHK","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"C52GSJWT","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:C52GSJWTPDGYNVHKJHSFRAUG4Y","target":"record","payload":{"canonical_record":{"source":{"id":"1805.10274","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2018-05-25T17:48:20Z","cross_cats_sorted":[],"title_canon_sha256":"f6fbf7e23f7e96381c0d54092210ddd9477409ce01c66082ab78c6fdeae84acc","abstract_canon_sha256":"091c499f553ef64d840914162ed32862b013da22512c393242b89ca4de058ac5"},"schema_version":"1.0"},"canonical_sha256":"17746926d378cd86d4ea49e4588286e615207a4be62d56fd6ec154749564e837","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:57.784785Z","signature_b64":"1d0e+mwQN1N2LfBvM1W7dnCFgWdOl0VsNF2/Y9iKMqwnw8+E4umqU2vaLTdgCt3sogV1LtwwaM8/DNuvwB8QBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17746926d378cd86d4ea49e4588286e615207a4be62d56fd6ec154749564e837","last_reissued_at":"2026-05-18T00:14:57.784009Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:57.784009Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.10274","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":"2026-05-18T00:14:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uEwmphmZfEZbnnhc5N07iDWgeH8hquWzCFHbAeqGGTeLinJHURDHc7DNbXDQFh31GZhLIFBXjVouzQ6Ag1FtBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T19:08:27.449169Z"},"content_sha256":"b36f36db4eabc199013686fda0be6b3f4bb84b87a98c5b119cbce61f6f5c290f","schema_version":"1.0","event_id":"sha256:b36f36db4eabc199013686fda0be6b3f4bb84b87a98c5b119cbce61f6f5c290f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:C52GSJWTPDGYNVHKJHSFRAUG4Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"UMDSub at SemEval-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ted Pedersen, Zhenduo Wang","submitted_at":"2018-05-25T17:48:20Z","abstract_excerpt":"This paper describes the UMDSub system that participated in Task 2 of SemEval-2018. We developed a system that predicts an emoji given the raw text in a English tweet. The system is a Multi-channel Convolutional Neural Network based on subword embeddings for the representation of tweets. This model improves on character or word based methods by about 2\\%. Our system placed 21st of 48 participating systems in the official evaluation."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10274","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":"2026-05-18T00:14:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zfvuGdJ1TYfzxYjwwqq8wmWFiL/gb4y6EJ5wYIkiiYblSHMNniKZo6P3I+HMQ73CtfMifgAFzwV+TxUPGWdYDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T19:08:27.449608Z"},"content_sha256":"c0bfc4134390cddc263c732b72ccdec451db4b410d552d321477ad62e6131f3c","schema_version":"1.0","event_id":"sha256:c0bfc4134390cddc263c732b72ccdec451db4b410d552d321477ad62e6131f3c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C52GSJWTPDGYNVHKJHSFRAUG4Y/bundle.json","state_url":"https://pith.science/pith/C52GSJWTPDGYNVHKJHSFRAUG4Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C52GSJWTPDGYNVHKJHSFRAUG4Y/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":"2026-06-07T19:08:27Z","links":{"resolver":"https://pith.science/pith/C52GSJWTPDGYNVHKJHSFRAUG4Y","bundle":"https://pith.science/pith/C52GSJWTPDGYNVHKJHSFRAUG4Y/bundle.json","state":"https://pith.science/pith/C52GSJWTPDGYNVHKJHSFRAUG4Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C52GSJWTPDGYNVHKJHSFRAUG4Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:C52GSJWTPDGYNVHKJHSFRAUG4Y","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":"091c499f553ef64d840914162ed32862b013da22512c393242b89ca4de058ac5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2018-05-25T17:48:20Z","title_canon_sha256":"f6fbf7e23f7e96381c0d54092210ddd9477409ce01c66082ab78c6fdeae84acc"},"schema_version":"1.0","source":{"id":"1805.10274","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.10274","created_at":"2026-05-18T00:14:57Z"},{"alias_kind":"arxiv_version","alias_value":"1805.10274v1","created_at":"2026-05-18T00:14:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10274","created_at":"2026-05-18T00:14:57Z"},{"alias_kind":"pith_short_12","alias_value":"C52GSJWTPDGY","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"C52GSJWTPDGYNVHK","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"C52GSJWT","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:c0bfc4134390cddc263c732b72ccdec451db4b410d552d321477ad62e6131f3c","target":"graph","created_at":"2026-05-18T00:14:57Z","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":"This paper describes the UMDSub system that participated in Task 2 of SemEval-2018. We developed a system that predicts an emoji given the raw text in a English tweet. The system is a Multi-channel Convolutional Neural Network based on subword embeddings for the representation of tweets. This model improves on character or word based methods by about 2\\%. Our system placed 21st of 48 participating systems in the official evaluation.","authors_text":"Ted Pedersen, Zhenduo Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2018-05-25T17:48:20Z","title":"UMDSub at SemEval-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10274","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:b36f36db4eabc199013686fda0be6b3f4bb84b87a98c5b119cbce61f6f5c290f","target":"record","created_at":"2026-05-18T00:14:57Z","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":"091c499f553ef64d840914162ed32862b013da22512c393242b89ca4de058ac5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2018-05-25T17:48:20Z","title_canon_sha256":"f6fbf7e23f7e96381c0d54092210ddd9477409ce01c66082ab78c6fdeae84acc"},"schema_version":"1.0","source":{"id":"1805.10274","kind":"arxiv","version":1}},"canonical_sha256":"17746926d378cd86d4ea49e4588286e615207a4be62d56fd6ec154749564e837","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"17746926d378cd86d4ea49e4588286e615207a4be62d56fd6ec154749564e837","first_computed_at":"2026-05-18T00:14:57.784009Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:57.784009Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1d0e+mwQN1N2LfBvM1W7dnCFgWdOl0VsNF2/Y9iKMqwnw8+E4umqU2vaLTdgCt3sogV1LtwwaM8/DNuvwB8QBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:57.784785Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.10274","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b36f36db4eabc199013686fda0be6b3f4bb84b87a98c5b119cbce61f6f5c290f","sha256:c0bfc4134390cddc263c732b72ccdec451db4b410d552d321477ad62e6131f3c"],"state_sha256":"e1a8fe8665f565761e0cf2e89bbd8e91e45bdafae8e939e2b54022b5fcad0850"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+tJgNfJhQJW9V7TTX+oww9EiNA+WEj+oY/uBt01JSDRBf533V1xIzPnuaa0GzeVesEspE30a2YQ5ykclGZIvCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T19:08:27.452269Z","bundle_sha256":"2cc1674135019c31f740d1b38f7d90c14b029da9f42127bfd30445f613926fa1"}}