{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:L6TSMYCCAB6BRI2MYGSLFPXXDV","short_pith_number":"pith:L6TSMYCC","canonical_record":{"source":{"id":"1706.09673","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-29T10:54:43Z","cross_cats_sorted":[],"title_canon_sha256":"4f61261645c8e999e5dd2eacd8bd5e5d77e789be2db602dfe2030c902bc4692e","abstract_canon_sha256":"fda65a4cee388c4286729f60f441839c96f304c1850834bb0e8c7f3ecb8d555a"},"schema_version":"1.0"},"canonical_sha256":"5fa7266042007c18a34cc1a4b2bef71d717d7b27188ccebbd42e0395ca1920c1","source":{"kind":"arxiv","id":"1706.09673","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.09673","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"arxiv_version","alias_value":"1706.09673v1","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.09673","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"pith_short_12","alias_value":"L6TSMYCCAB6B","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"L6TSMYCCAB6BRI2M","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"L6TSMYCC","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:L6TSMYCCAB6BRI2MYGSLFPXXDV","target":"record","payload":{"canonical_record":{"source":{"id":"1706.09673","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-29T10:54:43Z","cross_cats_sorted":[],"title_canon_sha256":"4f61261645c8e999e5dd2eacd8bd5e5d77e789be2db602dfe2030c902bc4692e","abstract_canon_sha256":"fda65a4cee388c4286729f60f441839c96f304c1850834bb0e8c7f3ecb8d555a"},"schema_version":"1.0"},"canonical_sha256":"5fa7266042007c18a34cc1a4b2bef71d717d7b27188ccebbd42e0395ca1920c1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:14.196555Z","signature_b64":"4W/591TSOYvlXWjRClRF0sCT6r6Ea3UcRKqKBAlK6Shzf2k4i/M/SxKEPemtHcGxZjGS1qI3FrT1zd5Bi1XiAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fa7266042007c18a34cc1a4b2bef71d717d7b27188ccebbd42e0395ca1920c1","last_reissued_at":"2026-05-18T00:41:14.195838Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:14.195838Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.09673","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":"2026-05-18T00:41:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jqsokKUfxvV70/MktINm5Oqm9iqrPeOxuxQ39IveC5eFfChdt0kapV80F1UBHhUgK/nDl209puhO2i4bnBlZAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T22:59:46.868140Z"},"content_sha256":"55e2cc41437d3573191ffa24750166b176da88c5a70b6be187a04d32afdc2a7b","schema_version":"1.0","event_id":"sha256:55e2cc41437d3573191ffa24750166b176da88c5a70b6be187a04d32afdc2a7b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:L6TSMYCCAB6BRI2MYGSLFPXXDV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Distributed Representations of Tweets - Present and Future","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ganesh J","submitted_at":"2017-06-29T10:54:43Z","abstract_excerpt":"Unsupervised representation learning for tweets is an important research field which helps in solving several business applications such as sentiment analysis, hashtag prediction, paraphrase detection and microblog ranking. A good tweet representation learning model must handle the idiosyncratic nature of tweets which poses several challenges such as short length, informal words, unusual grammar and misspellings. However, there is a lack of prior work which surveys the representation learning models with a focus on tweets. In this work, we organize the models based on its objective function wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.09673","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":"2026-05-18T00:41:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MfmFsQcPxYa0t2x0AfkdhPHIVpzd/RWJDHHcIaXl46okfxd4Qi00bm0EBbF/b6/4U+xrL5I5M30JnkkviEfVCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T22:59:46.868711Z"},"content_sha256":"832c7652d296ec3f51018797ae3103e4ee1590207736806a6ccb369790664c0d","schema_version":"1.0","event_id":"sha256:832c7652d296ec3f51018797ae3103e4ee1590207736806a6ccb369790664c0d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L6TSMYCCAB6BRI2MYGSLFPXXDV/bundle.json","state_url":"https://pith.science/pith/L6TSMYCCAB6BRI2MYGSLFPXXDV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L6TSMYCCAB6BRI2MYGSLFPXXDV/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":"2026-05-22T22:59:46Z","links":{"resolver":"https://pith.science/pith/L6TSMYCCAB6BRI2MYGSLFPXXDV","bundle":"https://pith.science/pith/L6TSMYCCAB6BRI2MYGSLFPXXDV/bundle.json","state":"https://pith.science/pith/L6TSMYCCAB6BRI2MYGSLFPXXDV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L6TSMYCCAB6BRI2MYGSLFPXXDV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:L6TSMYCCAB6BRI2MYGSLFPXXDV","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":"fda65a4cee388c4286729f60f441839c96f304c1850834bb0e8c7f3ecb8d555a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-29T10:54:43Z","title_canon_sha256":"4f61261645c8e999e5dd2eacd8bd5e5d77e789be2db602dfe2030c902bc4692e"},"schema_version":"1.0","source":{"id":"1706.09673","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.09673","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"arxiv_version","alias_value":"1706.09673v1","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.09673","created_at":"2026-05-18T00:41:14Z"},{"alias_kind":"pith_short_12","alias_value":"L6TSMYCCAB6B","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"L6TSMYCCAB6BRI2M","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"L6TSMYCC","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:832c7652d296ec3f51018797ae3103e4ee1590207736806a6ccb369790664c0d","target":"graph","created_at":"2026-05-18T00:41:14Z","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":"Unsupervised representation learning for tweets is an important research field which helps in solving several business applications such as sentiment analysis, hashtag prediction, paraphrase detection and microblog ranking. A good tweet representation learning model must handle the idiosyncratic nature of tweets which poses several challenges such as short length, informal words, unusual grammar and misspellings. However, there is a lack of prior work which surveys the representation learning models with a focus on tweets. In this work, we organize the models based on its objective function wh","authors_text":"Ganesh J","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-29T10:54:43Z","title":"Improving Distributed Representations of Tweets - Present and Future"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.09673","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":"sha256:55e2cc41437d3573191ffa24750166b176da88c5a70b6be187a04d32afdc2a7b","target":"record","created_at":"2026-05-18T00:41:14Z","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":"fda65a4cee388c4286729f60f441839c96f304c1850834bb0e8c7f3ecb8d555a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-29T10:54:43Z","title_canon_sha256":"4f61261645c8e999e5dd2eacd8bd5e5d77e789be2db602dfe2030c902bc4692e"},"schema_version":"1.0","source":{"id":"1706.09673","kind":"arxiv","version":1}},"canonical_sha256":"5fa7266042007c18a34cc1a4b2bef71d717d7b27188ccebbd42e0395ca1920c1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5fa7266042007c18a34cc1a4b2bef71d717d7b27188ccebbd42e0395ca1920c1","first_computed_at":"2026-05-18T00:41:14.195838Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:14.195838Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4W/591TSOYvlXWjRClRF0sCT6r6Ea3UcRKqKBAlK6Shzf2k4i/M/SxKEPemtHcGxZjGS1qI3FrT1zd5Bi1XiAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:14.196555Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.09673","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:55e2cc41437d3573191ffa24750166b176da88c5a70b6be187a04d32afdc2a7b","sha256:832c7652d296ec3f51018797ae3103e4ee1590207736806a6ccb369790664c0d"],"state_sha256":"90cb0819114069ba33b8165840b639d25c03bd0e8827cbc928fb7f9345a95c6f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yJtOaKi8FqNlySp+8A9LchHfEGtkyX0ng4NP8DyWlY8lL5f2pj4jNTLEX01QgoZhe+Jb/q+IB2sofBpU9nQABg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T22:59:46.871700Z","bundle_sha256":"049d4e73c0465a93a190421802032158fcb0b559825016447b1ce60dd62d23fa"}}