{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:6BIVKRUESILFAUGLQ7KLDSUOOP","short_pith_number":"pith:6BIVKRUE","schema_version":"1.0","canonical_sha256":"f05155468492165050cb87d4b1ca8e73e906589f1d6e3e2e8795319df640b9f4","source":{"kind":"arxiv","id":"1709.09220","version":1},"attestation_state":"computed","paper":{"title":"Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Andreea Hossmann, Athanasios Giannakopoulos, Claudiu Musat, Diego Antognini, Michael Baeriswyl","submitted_at":"2017-09-26T18:54:39Z","abstract_excerpt":"Aspect Term Extraction (ATE) detects opinionated aspect terms in sentences or text spans, with the end goal of performing aspect-based sentiment analysis. The small amount of available datasets for supervised ATE and the fact that they cover only a few domains raise the need for exploiting other data sources in new and creative ways. Publicly available review corpora contain a plethora of opinionated aspect terms and cover a larger domain spectrum. In this paper, we first propose a method for using such review corpora for creating a new dataset for ATE. Our method relies on an attention mechan"},"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":"1709.09220","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-09-26T18:54:39Z","cross_cats_sorted":[],"title_canon_sha256":"591a377b41f247a9a583f7b900f9c947fa7e3f4790e4c0d10c93df33ed6cf470","abstract_canon_sha256":"db38e49ffc222829faaf9ca1cbb9886216039a3116b258cefe95a3f1914d1f67"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:12.484010Z","signature_b64":"OlSfS0QLIozYSlMvovMk9iYeGergtLtwWEFErWNhg6knNtox7SiJp4sUJjCmHJ1AsaeBXMhkaaJGGpmDvsCRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f05155468492165050cb87d4b1ca8e73e906589f1d6e3e2e8795319df640b9f4","last_reissued_at":"2026-05-18T00:34:12.483370Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:12.483370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Andreea Hossmann, Athanasios Giannakopoulos, Claudiu Musat, Diego Antognini, Michael Baeriswyl","submitted_at":"2017-09-26T18:54:39Z","abstract_excerpt":"Aspect Term Extraction (ATE) detects opinionated aspect terms in sentences or text spans, with the end goal of performing aspect-based sentiment analysis. The small amount of available datasets for supervised ATE and the fact that they cover only a few domains raise the need for exploiting other data sources in new and creative ways. Publicly available review corpora contain a plethora of opinionated aspect terms and cover a larger domain spectrum. In this paper, we first propose a method for using such review corpora for creating a new dataset for ATE. Our method relies on an attention mechan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.09220","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":"1709.09220","created_at":"2026-05-18T00:34:12.483472+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.09220v1","created_at":"2026-05-18T00:34:12.483472+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.09220","created_at":"2026-05-18T00:34:12.483472+00:00"},{"alias_kind":"pith_short_12","alias_value":"6BIVKRUESILF","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"6BIVKRUESILFAUGL","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"6BIVKRUE","created_at":"2026-05-18T12:31:03.183658+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/6BIVKRUESILFAUGLQ7KLDSUOOP","json":"https://pith.science/pith/6BIVKRUESILFAUGLQ7KLDSUOOP.json","graph_json":"https://pith.science/api/pith-number/6BIVKRUESILFAUGLQ7KLDSUOOP/graph.json","events_json":"https://pith.science/api/pith-number/6BIVKRUESILFAUGLQ7KLDSUOOP/events.json","paper":"https://pith.science/paper/6BIVKRUE"},"agent_actions":{"view_html":"https://pith.science/pith/6BIVKRUESILFAUGLQ7KLDSUOOP","download_json":"https://pith.science/pith/6BIVKRUESILFAUGLQ7KLDSUOOP.json","view_paper":"https://pith.science/paper/6BIVKRUE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.09220&json=true","fetch_graph":"https://pith.science/api/pith-number/6BIVKRUESILFAUGLQ7KLDSUOOP/graph.json","fetch_events":"https://pith.science/api/pith-number/6BIVKRUESILFAUGLQ7KLDSUOOP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6BIVKRUESILFAUGLQ7KLDSUOOP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6BIVKRUESILFAUGLQ7KLDSUOOP/action/storage_attestation","attest_author":"https://pith.science/pith/6BIVKRUESILFAUGLQ7KLDSUOOP/action/author_attestation","sign_citation":"https://pith.science/pith/6BIVKRUESILFAUGLQ7KLDSUOOP/action/citation_signature","submit_replication":"https://pith.science/pith/6BIVKRUESILFAUGLQ7KLDSUOOP/action/replication_record"}},"created_at":"2026-05-18T00:34:12.483472+00:00","updated_at":"2026-05-18T00:34:12.483472+00:00"}