{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:OPB6IIYMS3WGT5CNAFJ2GLD7JY","short_pith_number":"pith:OPB6IIYM","schema_version":"1.0","canonical_sha256":"73c3e4230c96ec69f44d0153a32c7f4e2f22233267ca95caae98865480a8d52f","source":{"kind":"arxiv","id":"2208.09625","version":2},"attestation_state":"computed","paper":{"title":"SPOT: Knowledge-Enhanced Language Representations for Information Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Andrew Bartko, Chun-Nan Hsu, Ho-Cheol Kim, Jiacheng Li, Julian McAuley, Tyler Baldwin, Yannis Katsis","submitted_at":"2022-08-20T07:32:25Z","abstract_excerpt":"Knowledge-enhanced pre-trained models for language representation have been shown to be more effective in knowledge base construction tasks (i.e.,~relation extraction) than language models such as BERT. These knowledge-enhanced language models incorporate knowledge into pre-training to generate representations of entities or relationships. However, existing methods typically represent each entity with a separate embedding. As a result, these methods struggle to represent out-of-vocabulary entities and a large amount of parameters, on top of their underlying token models (i.e.,~the transformer)"},"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":"2208.09625","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-08-20T07:32:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"36c8a3bdb14759a82ec0aafaf5b094d05c49c5b98ea8808341aad0eac1550a3f","abstract_canon_sha256":"f49f74b8d1ffcfd9db37a89a86728f8154c16eaa05324f9d29d3c9e9be660e1b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:21.418078Z","signature_b64":"SC8iMAvNM0mkH2eYIB6esYoFuBp+sqhodXpN6Puk7CGI+bzdbOfrYpgvYL4pSo3FTbEVZlOsiUBYWAB/XnRbCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73c3e4230c96ec69f44d0153a32c7f4e2f22233267ca95caae98865480a8d52f","last_reissued_at":"2026-07-05T05:09:21.417623Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:21.417623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SPOT: Knowledge-Enhanced Language Representations for Information Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Andrew Bartko, Chun-Nan Hsu, Ho-Cheol Kim, Jiacheng Li, Julian McAuley, Tyler Baldwin, Yannis Katsis","submitted_at":"2022-08-20T07:32:25Z","abstract_excerpt":"Knowledge-enhanced pre-trained models for language representation have been shown to be more effective in knowledge base construction tasks (i.e.,~relation extraction) than language models such as BERT. These knowledge-enhanced language models incorporate knowledge into pre-training to generate representations of entities or relationships. However, existing methods typically represent each entity with a separate embedding. As a result, these methods struggle to represent out-of-vocabulary entities and a large amount of parameters, on top of their underlying token models (i.e.,~the transformer)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.09625","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/2208.09625/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":"2208.09625","created_at":"2026-07-05T05:09:21.417687+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.09625v2","created_at":"2026-07-05T05:09:21.417687+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.09625","created_at":"2026-07-05T05:09:21.417687+00:00"},{"alias_kind":"pith_short_12","alias_value":"OPB6IIYMS3WG","created_at":"2026-07-05T05:09:21.417687+00:00"},{"alias_kind":"pith_short_16","alias_value":"OPB6IIYMS3WGT5CN","created_at":"2026-07-05T05:09:21.417687+00:00"},{"alias_kind":"pith_short_8","alias_value":"OPB6IIYM","created_at":"2026-07-05T05:09:21.417687+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/OPB6IIYMS3WGT5CNAFJ2GLD7JY","json":"https://pith.science/pith/OPB6IIYMS3WGT5CNAFJ2GLD7JY.json","graph_json":"https://pith.science/api/pith-number/OPB6IIYMS3WGT5CNAFJ2GLD7JY/graph.json","events_json":"https://pith.science/api/pith-number/OPB6IIYMS3WGT5CNAFJ2GLD7JY/events.json","paper":"https://pith.science/paper/OPB6IIYM"},"agent_actions":{"view_html":"https://pith.science/pith/OPB6IIYMS3WGT5CNAFJ2GLD7JY","download_json":"https://pith.science/pith/OPB6IIYMS3WGT5CNAFJ2GLD7JY.json","view_paper":"https://pith.science/paper/OPB6IIYM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.09625&json=true","fetch_graph":"https://pith.science/api/pith-number/OPB6IIYMS3WGT5CNAFJ2GLD7JY/graph.json","fetch_events":"https://pith.science/api/pith-number/OPB6IIYMS3WGT5CNAFJ2GLD7JY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OPB6IIYMS3WGT5CNAFJ2GLD7JY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OPB6IIYMS3WGT5CNAFJ2GLD7JY/action/storage_attestation","attest_author":"https://pith.science/pith/OPB6IIYMS3WGT5CNAFJ2GLD7JY/action/author_attestation","sign_citation":"https://pith.science/pith/OPB6IIYMS3WGT5CNAFJ2GLD7JY/action/citation_signature","submit_replication":"https://pith.science/pith/OPB6IIYMS3WGT5CNAFJ2GLD7JY/action/replication_record"}},"created_at":"2026-07-05T05:09:21.417687+00:00","updated_at":"2026-07-05T05:09:21.417687+00:00"}