{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OWKXXBLPNMOBRDIUPGCLY6QSKR","short_pith_number":"pith:OWKXXBLP","schema_version":"1.0","canonical_sha256":"75957b856f6b1c188d147984bc7a125440af96a9763c62a73eda7464de656584","source":{"kind":"arxiv","id":"1906.09821","version":1},"attestation_state":"computed","paper":{"title":"Classification and Clustering of Arguments with Contextualized Word Embeddings","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Benjamin Schiller, Christian Stab, Iryna Gurevych, Johannes Daxenberger, Nils Reimers, Tilman Beck","submitted_at":"2019-06-24T09:55:21Z","abstract_excerpt":"We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. For argument classification, we improve the state-of-the-art for the UKP Sentential Argument Mining Corpus by 20.8 percentage points and for the IBM Debater - Evidence Sentences dataset by 7.4 percentage points. For the understudied task of argument cluster"},"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":"1906.09821","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2019-06-24T09:55:21Z","cross_cats_sorted":[],"title_canon_sha256":"73984c37f6c99aed84da6d538a32378f4f8d1bf75a8052fe741b71cb6cc3f1fb","abstract_canon_sha256":"2c75910d939ab17b15f2648366bd2082d79caf202db2928048d503e892638261"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:36.567392Z","signature_b64":"ImG7/VBsTXODc9S9dIdR5tKGv2rn7Dj83mnk9Uo7KL2RbBPFPvJPiHEgVz5oKUHxKY+WNIHEH2++y+yfk3UmAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75957b856f6b1c188d147984bc7a125440af96a9763c62a73eda7464de656584","last_reissued_at":"2026-05-17T23:42:36.566664Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:36.566664Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classification and Clustering of Arguments with Contextualized Word Embeddings","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Benjamin Schiller, Christian Stab, Iryna Gurevych, Johannes Daxenberger, Nils Reimers, Tilman Beck","submitted_at":"2019-06-24T09:55:21Z","abstract_excerpt":"We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. For argument classification, we improve the state-of-the-art for the UKP Sentential Argument Mining Corpus by 20.8 percentage points and for the IBM Debater - Evidence Sentences dataset by 7.4 percentage points. For the understudied task of argument cluster"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.09821","kind":"arxiv","version":1},"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":"1906.09821","created_at":"2026-05-17T23:42:36.566779+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.09821v1","created_at":"2026-05-17T23:42:36.566779+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.09821","created_at":"2026-05-17T23:42:36.566779+00:00"},{"alias_kind":"pith_short_12","alias_value":"OWKXXBLPNMOB","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"OWKXXBLPNMOBRDIU","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"OWKXXBLP","created_at":"2026-05-18T12:33:24.271573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.16852","citing_title":"A Community-Based Approach for Stance Distribution and Argument Organization","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OWKXXBLPNMOBRDIUPGCLY6QSKR","json":"https://pith.science/pith/OWKXXBLPNMOBRDIUPGCLY6QSKR.json","graph_json":"https://pith.science/api/pith-number/OWKXXBLPNMOBRDIUPGCLY6QSKR/graph.json","events_json":"https://pith.science/api/pith-number/OWKXXBLPNMOBRDIUPGCLY6QSKR/events.json","paper":"https://pith.science/paper/OWKXXBLP"},"agent_actions":{"view_html":"https://pith.science/pith/OWKXXBLPNMOBRDIUPGCLY6QSKR","download_json":"https://pith.science/pith/OWKXXBLPNMOBRDIUPGCLY6QSKR.json","view_paper":"https://pith.science/paper/OWKXXBLP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.09821&json=true","fetch_graph":"https://pith.science/api/pith-number/OWKXXBLPNMOBRDIUPGCLY6QSKR/graph.json","fetch_events":"https://pith.science/api/pith-number/OWKXXBLPNMOBRDIUPGCLY6QSKR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OWKXXBLPNMOBRDIUPGCLY6QSKR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OWKXXBLPNMOBRDIUPGCLY6QSKR/action/storage_attestation","attest_author":"https://pith.science/pith/OWKXXBLPNMOBRDIUPGCLY6QSKR/action/author_attestation","sign_citation":"https://pith.science/pith/OWKXXBLPNMOBRDIUPGCLY6QSKR/action/citation_signature","submit_replication":"https://pith.science/pith/OWKXXBLPNMOBRDIUPGCLY6QSKR/action/replication_record"}},"created_at":"2026-05-17T23:42:36.566779+00:00","updated_at":"2026-05-17T23:42:36.566779+00:00"}