{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HKVYLHU5CGSGGJEO4H5ZL2VVBV","short_pith_number":"pith:HKVYLHU5","schema_version":"1.0","canonical_sha256":"3aab859e9d11a463248ee1fb95eab50d46ba88615a852c70b5607a378c5aeade","source":{"kind":"arxiv","id":"1809.04838","version":1},"attestation_state":"computed","paper":{"title":"T\\\"ubingen-Oslo system: Linear regression works the best at Predicting Current and Future Psychological Health from Childhood Essays in the CLPsych 2018 Shared Task","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"\\c{C}a\\u{g}r{\\i} \\c{C}\\\"oltekin, Taraka Rama","submitted_at":"2018-09-13T08:53:14Z","abstract_excerpt":"This paper describes our efforts in predicting current and future psychological health from childhood essays within the scope of the CLPsych-2018 Shared Task. We experimented with a number of different models, including recurrent and convolutional networks, Poisson regression, support vector regression, and L1 and L2 regularized linear regression. We obtained the best results on the training/development data with L2 regularized linear regression (ridge regression) which also got the best scores on main metrics in the official testing for task A (predicting psychological health from essays writ"},"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":"1809.04838","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-09-13T08:53:14Z","cross_cats_sorted":[],"title_canon_sha256":"acc657a24cd21a9152b38bf88169c68cfa781b127fd5dbed0d1ce95c1ca6c314","abstract_canon_sha256":"d573a70c41512eeb252be962612a2bb6fc5f0c6637ae1d37dab538a0cd633984"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:47.368493Z","signature_b64":"dE+tjY+GzfOumtP9cuBupfl3GkPbQCCAoZIR3gluUqmnPNFZQsEMLn9h+KUftBERms5eA4w9uhVlh5gZFYcVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3aab859e9d11a463248ee1fb95eab50d46ba88615a852c70b5607a378c5aeade","last_reissued_at":"2026-05-18T00:05:47.367951Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:47.367951Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"T\\\"ubingen-Oslo system: Linear regression works the best at Predicting Current and Future Psychological Health from Childhood Essays in the CLPsych 2018 Shared Task","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"\\c{C}a\\u{g}r{\\i} \\c{C}\\\"oltekin, Taraka Rama","submitted_at":"2018-09-13T08:53:14Z","abstract_excerpt":"This paper describes our efforts in predicting current and future psychological health from childhood essays within the scope of the CLPsych-2018 Shared Task. We experimented with a number of different models, including recurrent and convolutional networks, Poisson regression, support vector regression, and L1 and L2 regularized linear regression. We obtained the best results on the training/development data with L2 regularized linear regression (ridge regression) which also got the best scores on main metrics in the official testing for task A (predicting psychological health from essays writ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04838","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":"1809.04838","created_at":"2026-05-18T00:05:47.368041+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.04838v1","created_at":"2026-05-18T00:05:47.368041+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04838","created_at":"2026-05-18T00:05:47.368041+00:00"},{"alias_kind":"pith_short_12","alias_value":"HKVYLHU5CGSG","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"HKVYLHU5CGSGGJEO","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"HKVYLHU5","created_at":"2026-05-18T12:32:28.185984+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/HKVYLHU5CGSGGJEO4H5ZL2VVBV","json":"https://pith.science/pith/HKVYLHU5CGSGGJEO4H5ZL2VVBV.json","graph_json":"https://pith.science/api/pith-number/HKVYLHU5CGSGGJEO4H5ZL2VVBV/graph.json","events_json":"https://pith.science/api/pith-number/HKVYLHU5CGSGGJEO4H5ZL2VVBV/events.json","paper":"https://pith.science/paper/HKVYLHU5"},"agent_actions":{"view_html":"https://pith.science/pith/HKVYLHU5CGSGGJEO4H5ZL2VVBV","download_json":"https://pith.science/pith/HKVYLHU5CGSGGJEO4H5ZL2VVBV.json","view_paper":"https://pith.science/paper/HKVYLHU5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.04838&json=true","fetch_graph":"https://pith.science/api/pith-number/HKVYLHU5CGSGGJEO4H5ZL2VVBV/graph.json","fetch_events":"https://pith.science/api/pith-number/HKVYLHU5CGSGGJEO4H5ZL2VVBV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HKVYLHU5CGSGGJEO4H5ZL2VVBV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HKVYLHU5CGSGGJEO4H5ZL2VVBV/action/storage_attestation","attest_author":"https://pith.science/pith/HKVYLHU5CGSGGJEO4H5ZL2VVBV/action/author_attestation","sign_citation":"https://pith.science/pith/HKVYLHU5CGSGGJEO4H5ZL2VVBV/action/citation_signature","submit_replication":"https://pith.science/pith/HKVYLHU5CGSGGJEO4H5ZL2VVBV/action/replication_record"}},"created_at":"2026-05-18T00:05:47.368041+00:00","updated_at":"2026-05-18T00:05:47.368041+00:00"}