{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:MMPQANW5GVCJJKY6PG4DMBSQZ3","short_pith_number":"pith:MMPQANW5","schema_version":"1.0","canonical_sha256":"631f0036dd354494ab1e79b8360650cec93c8284c30c9756c1dd78a47ee409e9","source":{"kind":"arxiv","id":"2109.01048","version":3},"attestation_state":"computed","paper":{"title":"Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hao Peng, Hongyin Zhu, Jinghui Xiao, Juanzi Li, Lei Hou, Zhiheng Lyu","submitted_at":"2021-09-02T16:05:24Z","abstract_excerpt":"Existing technologies expand BERT from different perspectives, e.g. designing different pre-training tasks, different semantic granularities, and different model architectures. Few models consider expanding BERT from different text formats. In this paper, we propose a heterogeneous knowledge language model (\\textbf{HKLM}), a unified pre-trained language model (PLM) for all forms of text, including unstructured text, semi-structured text, and well-structured text. To capture the corresponding relations among these multi-format knowledge, our approach uses masked language model objective to lear"},"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":"2109.01048","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-02T16:05:24Z","cross_cats_sorted":[],"title_canon_sha256":"241e7308d1ce2a593934946778e2e3f28e647adcce66c524861c74be32ec95fa","abstract_canon_sha256":"f0d06511f712f5b62dfbeb5e8a3c352f398a759d5ec0303727afc3dd108ffb17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:58:41.898905Z","signature_b64":"F2vz/8PlQilwsWA+mc7qMGFNsSq0cujerYnnpNK94H3PSMk4PV1IB7wEB51LoxvB6YzyBSjx4f7bZm8chdCxBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"631f0036dd354494ab1e79b8360650cec93c8284c30c9756c1dd78a47ee409e9","last_reissued_at":"2026-07-05T07:58:41.898377Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:58:41.898377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hao Peng, Hongyin Zhu, Jinghui Xiao, Juanzi Li, Lei Hou, Zhiheng Lyu","submitted_at":"2021-09-02T16:05:24Z","abstract_excerpt":"Existing technologies expand BERT from different perspectives, e.g. designing different pre-training tasks, different semantic granularities, and different model architectures. Few models consider expanding BERT from different text formats. In this paper, we propose a heterogeneous knowledge language model (\\textbf{HKLM}), a unified pre-trained language model (PLM) for all forms of text, including unstructured text, semi-structured text, and well-structured text. To capture the corresponding relations among these multi-format knowledge, our approach uses masked language model objective to lear"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.01048","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.01048/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":"2109.01048","created_at":"2026-07-05T07:58:41.898437+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.01048v3","created_at":"2026-07-05T07:58:41.898437+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.01048","created_at":"2026-07-05T07:58:41.898437+00:00"},{"alias_kind":"pith_short_12","alias_value":"MMPQANW5GVCJ","created_at":"2026-07-05T07:58:41.898437+00:00"},{"alias_kind":"pith_short_16","alias_value":"MMPQANW5GVCJJKY6","created_at":"2026-07-05T07:58:41.898437+00:00"},{"alias_kind":"pith_short_8","alias_value":"MMPQANW5","created_at":"2026-07-05T07:58:41.898437+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/MMPQANW5GVCJJKY6PG4DMBSQZ3","json":"https://pith.science/pith/MMPQANW5GVCJJKY6PG4DMBSQZ3.json","graph_json":"https://pith.science/api/pith-number/MMPQANW5GVCJJKY6PG4DMBSQZ3/graph.json","events_json":"https://pith.science/api/pith-number/MMPQANW5GVCJJKY6PG4DMBSQZ3/events.json","paper":"https://pith.science/paper/MMPQANW5"},"agent_actions":{"view_html":"https://pith.science/pith/MMPQANW5GVCJJKY6PG4DMBSQZ3","download_json":"https://pith.science/pith/MMPQANW5GVCJJKY6PG4DMBSQZ3.json","view_paper":"https://pith.science/paper/MMPQANW5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.01048&json=true","fetch_graph":"https://pith.science/api/pith-number/MMPQANW5GVCJJKY6PG4DMBSQZ3/graph.json","fetch_events":"https://pith.science/api/pith-number/MMPQANW5GVCJJKY6PG4DMBSQZ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MMPQANW5GVCJJKY6PG4DMBSQZ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MMPQANW5GVCJJKY6PG4DMBSQZ3/action/storage_attestation","attest_author":"https://pith.science/pith/MMPQANW5GVCJJKY6PG4DMBSQZ3/action/author_attestation","sign_citation":"https://pith.science/pith/MMPQANW5GVCJJKY6PG4DMBSQZ3/action/citation_signature","submit_replication":"https://pith.science/pith/MMPQANW5GVCJJKY6PG4DMBSQZ3/action/replication_record"}},"created_at":"2026-07-05T07:58:41.898437+00:00","updated_at":"2026-07-05T07:58:41.898437+00:00"}