{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RFJEXFN237YDSIFT4ZIIP6F773","short_pith_number":"pith:RFJEXFN2","schema_version":"1.0","canonical_sha256":"89524b95badff03920b3e65087f8bffee50bc70e6565c717def18ef2833c2a59","source":{"kind":"arxiv","id":"2106.06944","version":2},"attestation_state":"computed","paper":{"title":"SASICM A Multi-Task Benchmark For Subtext Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Feng Han, Furao Shen, Hua Yan, Jian Zhao, Junyi An, Weikang Xiao","submitted_at":"2021-06-13T08:29:15Z","abstract_excerpt":"Subtext is a kind of deep semantics which can be acquired after one or more rounds of expression transformation. As a popular way of expressing one's intentions, it is well worth studying. In this paper, we try to make computers understand whether there is a subtext by means of machine learning. We build a Chinese dataset whose source data comes from the popular social media (e.g. Weibo, Netease Music, Zhihu, and Bilibili). In addition, we also build a baseline model called SASICM to deal with subtext recognition. The F1 score of SASICMg, whose pretrained model is GloVe, is as high as 64.37%, "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2106.06944","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-13T08:29:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4188dbbfc8abc4fd12907e89c7fc4359292267d72fc504f3b0e989d51f424213","abstract_canon_sha256":"6f841aafe2e2625e53716a3f2d68dd7e58751a7a92c877080a4543d252ffd319"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:54:56.516740Z","signature_b64":"UCg2cZqcRUZY3vQZK4ivLjNvsEpTu6z8VG1kS54ffL4oIc0fthehrcQR8EfPYDhd54SBmZxiIq57W6TehT6wCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89524b95badff03920b3e65087f8bffee50bc70e6565c717def18ef2833c2a59","last_reissued_at":"2026-07-05T02:54:56.516178Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:54:56.516178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SASICM A Multi-Task Benchmark For Subtext Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Feng Han, Furao Shen, Hua Yan, Jian Zhao, Junyi An, Weikang Xiao","submitted_at":"2021-06-13T08:29:15Z","abstract_excerpt":"Subtext is a kind of deep semantics which can be acquired after one or more rounds of expression transformation. As a popular way of expressing one's intentions, it is well worth studying. In this paper, we try to make computers understand whether there is a subtext by means of machine learning. We build a Chinese dataset whose source data comes from the popular social media (e.g. Weibo, Netease Music, Zhihu, and Bilibili). In addition, we also build a baseline model called SASICM to deal with subtext recognition. The F1 score of SASICMg, whose pretrained model is GloVe, is as high as 64.37%, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.06944","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2106.06944/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2106.06944","created_at":"2026-07-05T02:54:56.516238+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.06944v2","created_at":"2026-07-05T02:54:56.516238+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.06944","created_at":"2026-07-05T02:54:56.516238+00:00"},{"alias_kind":"pith_short_12","alias_value":"RFJEXFN237YD","created_at":"2026-07-05T02:54:56.516238+00:00"},{"alias_kind":"pith_short_16","alias_value":"RFJEXFN237YDSIFT","created_at":"2026-07-05T02:54:56.516238+00:00"},{"alias_kind":"pith_short_8","alias_value":"RFJEXFN2","created_at":"2026-07-05T02:54:56.516238+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RFJEXFN237YDSIFT4ZIIP6F773","json":"https://pith.science/pith/RFJEXFN237YDSIFT4ZIIP6F773.json","graph_json":"https://pith.science/api/pith-number/RFJEXFN237YDSIFT4ZIIP6F773/graph.json","events_json":"https://pith.science/api/pith-number/RFJEXFN237YDSIFT4ZIIP6F773/events.json","paper":"https://pith.science/paper/RFJEXFN2"},"agent_actions":{"view_html":"https://pith.science/pith/RFJEXFN237YDSIFT4ZIIP6F773","download_json":"https://pith.science/pith/RFJEXFN237YDSIFT4ZIIP6F773.json","view_paper":"https://pith.science/paper/RFJEXFN2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.06944&json=true","fetch_graph":"https://pith.science/api/pith-number/RFJEXFN237YDSIFT4ZIIP6F773/graph.json","fetch_events":"https://pith.science/api/pith-number/RFJEXFN237YDSIFT4ZIIP6F773/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RFJEXFN237YDSIFT4ZIIP6F773/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RFJEXFN237YDSIFT4ZIIP6F773/action/storage_attestation","attest_author":"https://pith.science/pith/RFJEXFN237YDSIFT4ZIIP6F773/action/author_attestation","sign_citation":"https://pith.science/pith/RFJEXFN237YDSIFT4ZIIP6F773/action/citation_signature","submit_replication":"https://pith.science/pith/RFJEXFN237YDSIFT4ZIIP6F773/action/replication_record"}},"created_at":"2026-07-05T02:54:56.516238+00:00","updated_at":"2026-07-05T02:54:56.516238+00:00"}