{"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"}