{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:XJ4KJTKMWQRAEOYAZIS7V5YIRI","short_pith_number":"pith:XJ4KJTKM","schema_version":"1.0","canonical_sha256":"ba78a4cd4cb422023b00ca25faf7088a34f37e03a8a4dd550104b1ba5970fefe","source":{"kind":"arxiv","id":"1812.08951","version":2},"attestation_state":"computed","paper":{"title":"Analysis Methods in Neural Language Processing: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CL","authors_text":"James Glass, Yonatan Belinkov","submitted_at":"2018-12-21T05:13:03Z","abstract_excerpt":"The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential direction"},"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":"1812.08951","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-12-21T05:13:03Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"7b7f8ce2dbd1eb16e9e310cb87e95c5512f8a132ceb4e178836c7403001c3877","abstract_canon_sha256":"0691994db916736c3971b63eb15a456ab699429843d5d5957ceb8f784ea96a21"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:27.398232Z","signature_b64":"KgEvVEbZ/zgOVVnWyT8EhNSduqJjAzMe7oVmEuXPUCwuA9l7X32p6jrMTD2WnefVOQIHGMNB5VSBLCnAm8fJCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba78a4cd4cb422023b00ca25faf7088a34f37e03a8a4dd550104b1ba5970fefe","last_reissued_at":"2026-05-17T23:56:27.397774Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:27.397774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analysis Methods in Neural Language Processing: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CL","authors_text":"James Glass, Yonatan Belinkov","submitted_at":"2018-12-21T05:13:03Z","abstract_excerpt":"The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential direction"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.08951","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":""},"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":"1812.08951","created_at":"2026-05-17T23:56:27.397845+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.08951v2","created_at":"2026-05-17T23:56:27.397845+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.08951","created_at":"2026-05-17T23:56:27.397845+00:00"},{"alias_kind":"pith_short_12","alias_value":"XJ4KJTKMWQRA","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"XJ4KJTKMWQRAEOYA","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"XJ4KJTKM","created_at":"2026-05-18T12:33:01.666342+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.09881","citing_title":"Toward Calibrated, Fair, and accurate Deepfake Detection","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XJ4KJTKMWQRAEOYAZIS7V5YIRI","json":"https://pith.science/pith/XJ4KJTKMWQRAEOYAZIS7V5YIRI.json","graph_json":"https://pith.science/api/pith-number/XJ4KJTKMWQRAEOYAZIS7V5YIRI/graph.json","events_json":"https://pith.science/api/pith-number/XJ4KJTKMWQRAEOYAZIS7V5YIRI/events.json","paper":"https://pith.science/paper/XJ4KJTKM"},"agent_actions":{"view_html":"https://pith.science/pith/XJ4KJTKMWQRAEOYAZIS7V5YIRI","download_json":"https://pith.science/pith/XJ4KJTKMWQRAEOYAZIS7V5YIRI.json","view_paper":"https://pith.science/paper/XJ4KJTKM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.08951&json=true","fetch_graph":"https://pith.science/api/pith-number/XJ4KJTKMWQRAEOYAZIS7V5YIRI/graph.json","fetch_events":"https://pith.science/api/pith-number/XJ4KJTKMWQRAEOYAZIS7V5YIRI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XJ4KJTKMWQRAEOYAZIS7V5YIRI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XJ4KJTKMWQRAEOYAZIS7V5YIRI/action/storage_attestation","attest_author":"https://pith.science/pith/XJ4KJTKMWQRAEOYAZIS7V5YIRI/action/author_attestation","sign_citation":"https://pith.science/pith/XJ4KJTKMWQRAEOYAZIS7V5YIRI/action/citation_signature","submit_replication":"https://pith.science/pith/XJ4KJTKMWQRAEOYAZIS7V5YIRI/action/replication_record"}},"created_at":"2026-05-17T23:56:27.397845+00:00","updated_at":"2026-05-17T23:56:27.397845+00:00"}