{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:SNQABEQ6UND7PM643PI4N4SKZA","short_pith_number":"pith:SNQABEQ6","schema_version":"1.0","canonical_sha256":"936000921ea347f7b3dcdbd1c6f24ac80ff2cb3007ae17cf945f7abb21d0847f","source":{"kind":"arxiv","id":"2002.06544","version":1},"attestation_state":"computed","paper":{"title":"Exploring Neural Models for Parsing Natural Language into First-Order Logic","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Balaji Krishnamurthy, Hrituraj Singh, Milan Aggrawal","submitted_at":"2020-02-16T09:22:32Z","abstract_excerpt":"Semantic parsing is the task of obtaining machine-interpretable representations from natural language text. We consider one such formal representation - First-Order Logic (FOL) and explore the capability of neural models in parsing English sentences to FOL. We model FOL parsing as a sequence to sequence mapping task where given a natural language sentence, it is encoded into an intermediate representation using an LSTM followed by a decoder which sequentially generates the predicates in the corresponding FOL formula. We improve the standard encoder-decoder model by introducing a variable align"},"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":"2002.06544","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-02-16T09:22:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"77711e2192b0d5eb610dbc6e6cc619949d76fab91b61b747c62a47b54bdc7971","abstract_canon_sha256":"8a703feb62eef5967f03363b3b3ef8b0459e035167d40c68d7da688ff392e32a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:41:07.232242Z","signature_b64":"LzxSZtUQVsN9WXYjJmmGtvDd2eWyJenAPwUYjOfPIdifAuz/S/eCLY7vTvrfrFrXpN5OKTIh6xNbyjsbxWNkAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"936000921ea347f7b3dcdbd1c6f24ac80ff2cb3007ae17cf945f7abb21d0847f","last_reissued_at":"2026-07-05T00:41:07.231807Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:41:07.231807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring Neural Models for Parsing Natural Language into First-Order Logic","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Balaji Krishnamurthy, Hrituraj Singh, Milan Aggrawal","submitted_at":"2020-02-16T09:22:32Z","abstract_excerpt":"Semantic parsing is the task of obtaining machine-interpretable representations from natural language text. We consider one such formal representation - First-Order Logic (FOL) and explore the capability of neural models in parsing English sentences to FOL. We model FOL parsing as a sequence to sequence mapping task where given a natural language sentence, it is encoded into an intermediate representation using an LSTM followed by a decoder which sequentially generates the predicates in the corresponding FOL formula. We improve the standard encoder-decoder model by introducing a variable align"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.06544","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2002.06544/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":"2002.06544","created_at":"2026-07-05T00:41:07.231876+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.06544v1","created_at":"2026-07-05T00:41:07.231876+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.06544","created_at":"2026-07-05T00:41:07.231876+00:00"},{"alias_kind":"pith_short_12","alias_value":"SNQABEQ6UND7","created_at":"2026-07-05T00:41:07.231876+00:00"},{"alias_kind":"pith_short_16","alias_value":"SNQABEQ6UND7PM64","created_at":"2026-07-05T00:41:07.231876+00:00"},{"alias_kind":"pith_short_8","alias_value":"SNQABEQ6","created_at":"2026-07-05T00:41:07.231876+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.02837","citing_title":"Fixing FOLIO and MALLS: Verified Annotations and an LLM-assisted Framework to Focus Human Relabeling","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09567","citing_title":"Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions","ref_index":39,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SNQABEQ6UND7PM643PI4N4SKZA","json":"https://pith.science/pith/SNQABEQ6UND7PM643PI4N4SKZA.json","graph_json":"https://pith.science/api/pith-number/SNQABEQ6UND7PM643PI4N4SKZA/graph.json","events_json":"https://pith.science/api/pith-number/SNQABEQ6UND7PM643PI4N4SKZA/events.json","paper":"https://pith.science/paper/SNQABEQ6"},"agent_actions":{"view_html":"https://pith.science/pith/SNQABEQ6UND7PM643PI4N4SKZA","download_json":"https://pith.science/pith/SNQABEQ6UND7PM643PI4N4SKZA.json","view_paper":"https://pith.science/paper/SNQABEQ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.06544&json=true","fetch_graph":"https://pith.science/api/pith-number/SNQABEQ6UND7PM643PI4N4SKZA/graph.json","fetch_events":"https://pith.science/api/pith-number/SNQABEQ6UND7PM643PI4N4SKZA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SNQABEQ6UND7PM643PI4N4SKZA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SNQABEQ6UND7PM643PI4N4SKZA/action/storage_attestation","attest_author":"https://pith.science/pith/SNQABEQ6UND7PM643PI4N4SKZA/action/author_attestation","sign_citation":"https://pith.science/pith/SNQABEQ6UND7PM643PI4N4SKZA/action/citation_signature","submit_replication":"https://pith.science/pith/SNQABEQ6UND7PM643PI4N4SKZA/action/replication_record"}},"created_at":"2026-07-05T00:41:07.231876+00:00","updated_at":"2026-07-05T00:41:07.231876+00:00"}