{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:3UWW4JL6GCOCI7I7B7ECWJMSF2","short_pith_number":"pith:3UWW4JL6","schema_version":"1.0","canonical_sha256":"dd2d6e257e309c247d1f0fc82b25922e91a34eadb379dde8fef2f4d44e0f58ba","source":{"kind":"arxiv","id":"1708.04358","version":1},"attestation_state":"computed","paper":{"title":"Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.SI"],"primary_cat":"cs.CL","authors_text":"Afshin Rahimi, Timothy Baldwin, Trevor Cohn","submitted_at":"2017-08-14T23:52:02Z","abstract_excerpt":"We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset."},"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":"1708.04358","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-08-14T23:52:02Z","cross_cats_sorted":["cs.IR","cs.SI"],"title_canon_sha256":"bf0c60617a223039c9c142ab7525fb71b5353a0b545e01b6f1d2eaeb57c5b90f","abstract_canon_sha256":"f42f51b48ac7b2e1c09a219ddcd29678ba15ac5f98290cfb438c1ec4b2136ae9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:37:59.554585Z","signature_b64":"I+YEnSFVENOhOzD+OmJMaqyTUwpqIq4EF6zrg/smFGMMQi0TazhifBmD6wFhGmXKSWShW/SH2TW6rw0PP5IhBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd2d6e257e309c247d1f0fc82b25922e91a34eadb379dde8fef2f4d44e0f58ba","last_reissued_at":"2026-05-18T00:37:59.553998Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:37:59.553998Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.SI"],"primary_cat":"cs.CL","authors_text":"Afshin Rahimi, Timothy Baldwin, Trevor Cohn","submitted_at":"2017-08-14T23:52:02Z","abstract_excerpt":"We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04358","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":"1708.04358","created_at":"2026-05-18T00:37:59.554076+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.04358v1","created_at":"2026-05-18T00:37:59.554076+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04358","created_at":"2026-05-18T00:37:59.554076+00:00"},{"alias_kind":"pith_short_12","alias_value":"3UWW4JL6GCOC","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"3UWW4JL6GCOCI7I7","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"3UWW4JL6","created_at":"2026-05-18T12:30:58.224056+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/3UWW4JL6GCOCI7I7B7ECWJMSF2","json":"https://pith.science/pith/3UWW4JL6GCOCI7I7B7ECWJMSF2.json","graph_json":"https://pith.science/api/pith-number/3UWW4JL6GCOCI7I7B7ECWJMSF2/graph.json","events_json":"https://pith.science/api/pith-number/3UWW4JL6GCOCI7I7B7ECWJMSF2/events.json","paper":"https://pith.science/paper/3UWW4JL6"},"agent_actions":{"view_html":"https://pith.science/pith/3UWW4JL6GCOCI7I7B7ECWJMSF2","download_json":"https://pith.science/pith/3UWW4JL6GCOCI7I7B7ECWJMSF2.json","view_paper":"https://pith.science/paper/3UWW4JL6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.04358&json=true","fetch_graph":"https://pith.science/api/pith-number/3UWW4JL6GCOCI7I7B7ECWJMSF2/graph.json","fetch_events":"https://pith.science/api/pith-number/3UWW4JL6GCOCI7I7B7ECWJMSF2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3UWW4JL6GCOCI7I7B7ECWJMSF2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3UWW4JL6GCOCI7I7B7ECWJMSF2/action/storage_attestation","attest_author":"https://pith.science/pith/3UWW4JL6GCOCI7I7B7ECWJMSF2/action/author_attestation","sign_citation":"https://pith.science/pith/3UWW4JL6GCOCI7I7B7ECWJMSF2/action/citation_signature","submit_replication":"https://pith.science/pith/3UWW4JL6GCOCI7I7B7ECWJMSF2/action/replication_record"}},"created_at":"2026-05-18T00:37:59.554076+00:00","updated_at":"2026-05-18T00:37:59.554076+00:00"}