{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:UEKAYJIASL42JVVIE6I4KXT2JG","short_pith_number":"pith:UEKAYJIA","schema_version":"1.0","canonical_sha256":"a1140c250092f9a4d6a82791c55e7a499615757d1eb27ad949e10941773620c8","source":{"kind":"arxiv","id":"1512.03465","version":3},"attestation_state":"computed","paper":{"title":"Mined Semantic Analysis: A New Concept Space Model for Semantic Representation of Textual Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Walid Shalaby, Wlodek Zadrozny","submitted_at":"2015-12-10T22:15:10Z","abstract_excerpt":"Mined Semantic Analysis (MSA) is a novel concept space model which employs unsupervised learning to generate semantic representations of text. MSA represents textual structures (terms, phrases, documents) as a Bag of Concepts (BoC) where concepts are derived from concept rich encyclopedic corpora. Traditional concept space models exploit only target corpus content to construct the concept space. MSA, alternatively, uncovers implicit relations between concepts by mining for their associations (e.g., mining Wikipedia's \"See also\" link graph). We evaluate MSA's performance on benchmark datasets f"},"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":"1512.03465","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-12-10T22:15:10Z","cross_cats_sorted":[],"title_canon_sha256":"55426d132d9e9495bf1c2ee062fe19081714bdd6bb84623037c41eedbbb76b0e","abstract_canon_sha256":"28e5da82f5cbf1ebf9d5d9fd68f01b78b80a0e62c5b8ae467e31e389f5fb01a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:03.118006Z","signature_b64":"tpk+AzZexG2F1/YxUqqTgzH7gVyTCE0eZ6jo/127j6lJcwSR/jJpk+iIXs1ikLrvmtXc9FwzsNf2N+ZROjvCBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1140c250092f9a4d6a82791c55e7a499615757d1eb27ad949e10941773620c8","last_reissued_at":"2026-05-18T00:27:03.117255Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:03.117255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mined Semantic Analysis: A New Concept Space Model for Semantic Representation of Textual Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Walid Shalaby, Wlodek Zadrozny","submitted_at":"2015-12-10T22:15:10Z","abstract_excerpt":"Mined Semantic Analysis (MSA) is a novel concept space model which employs unsupervised learning to generate semantic representations of text. MSA represents textual structures (terms, phrases, documents) as a Bag of Concepts (BoC) where concepts are derived from concept rich encyclopedic corpora. Traditional concept space models exploit only target corpus content to construct the concept space. MSA, alternatively, uncovers implicit relations between concepts by mining for their associations (e.g., mining Wikipedia's \"See also\" link graph). We evaluate MSA's performance on benchmark datasets f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.03465","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1512.03465","created_at":"2026-05-18T00:27:03.117500+00:00"},{"alias_kind":"arxiv_version","alias_value":"1512.03465v3","created_at":"2026-05-18T00:27:03.117500+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.03465","created_at":"2026-05-18T00:27:03.117500+00:00"},{"alias_kind":"pith_short_12","alias_value":"UEKAYJIASL42","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_16","alias_value":"UEKAYJIASL42JVVI","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_8","alias_value":"UEKAYJIA","created_at":"2026-05-18T12:29:44.643036+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/UEKAYJIASL42JVVIE6I4KXT2JG","json":"https://pith.science/pith/UEKAYJIASL42JVVIE6I4KXT2JG.json","graph_json":"https://pith.science/api/pith-number/UEKAYJIASL42JVVIE6I4KXT2JG/graph.json","events_json":"https://pith.science/api/pith-number/UEKAYJIASL42JVVIE6I4KXT2JG/events.json","paper":"https://pith.science/paper/UEKAYJIA"},"agent_actions":{"view_html":"https://pith.science/pith/UEKAYJIASL42JVVIE6I4KXT2JG","download_json":"https://pith.science/pith/UEKAYJIASL42JVVIE6I4KXT2JG.json","view_paper":"https://pith.science/paper/UEKAYJIA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1512.03465&json=true","fetch_graph":"https://pith.science/api/pith-number/UEKAYJIASL42JVVIE6I4KXT2JG/graph.json","fetch_events":"https://pith.science/api/pith-number/UEKAYJIASL42JVVIE6I4KXT2JG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UEKAYJIASL42JVVIE6I4KXT2JG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UEKAYJIASL42JVVIE6I4KXT2JG/action/storage_attestation","attest_author":"https://pith.science/pith/UEKAYJIASL42JVVIE6I4KXT2JG/action/author_attestation","sign_citation":"https://pith.science/pith/UEKAYJIASL42JVVIE6I4KXT2JG/action/citation_signature","submit_replication":"https://pith.science/pith/UEKAYJIASL42JVVIE6I4KXT2JG/action/replication_record"}},"created_at":"2026-05-18T00:27:03.117500+00:00","updated_at":"2026-05-18T00:27:03.117500+00:00"}