{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:B6EFG7UIBV52T6ARXOHWUFKAUP","short_pith_number":"pith:B6EFG7UI","schema_version":"1.0","canonical_sha256":"0f88537e880d7ba9f811bb8f6a1540a3c47a7cb4e785b7edabd87e1f14ea8a2a","source":{"kind":"arxiv","id":"1904.01451","version":1},"attestation_state":"computed","paper":{"title":"Using Multi-Sense Vector Embeddings for Reverse Dictionaries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Andrew Yates, Dietrich Klakow, Gerard de Melo, Michael A. Hedderich","submitted_at":"2019-04-02T14:17:19Z","abstract_excerpt":"Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architectu"},"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":"1904.01451","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-02T14:17:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d25de02b5cadc20a0f83c1019f6a42f73ca890af0585cb13c84c9f210ac954ff","abstract_canon_sha256":"2ff2b37457f8e143713c7e51194847212fba43212970b9395ad81a433220ba21"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:34.587704Z","signature_b64":"ZIY3nbmg2oxwCNILN4IHz4ORPu1wCSOZ+EePUXH9yzzylK8c3qV9fpsMJAGVnEkYqa6jZMlkeQaYamg4t+TIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f88537e880d7ba9f811bb8f6a1540a3c47a7cb4e785b7edabd87e1f14ea8a2a","last_reissued_at":"2026-05-17T23:49:34.587067Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:34.587067Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using Multi-Sense Vector Embeddings for Reverse Dictionaries","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Andrew Yates, Dietrich Klakow, Gerard de Melo, Michael A. Hedderich","submitted_at":"2019-04-02T14:17:19Z","abstract_excerpt":"Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architectu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.01451","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":"1904.01451","created_at":"2026-05-17T23:49:34.587154+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.01451v1","created_at":"2026-05-17T23:49:34.587154+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.01451","created_at":"2026-05-17T23:49:34.587154+00:00"},{"alias_kind":"pith_short_12","alias_value":"B6EFG7UIBV52","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"B6EFG7UIBV52T6AR","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"B6EFG7UI","created_at":"2026-05-18T12:33:12.712433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.21475","citing_title":"Advancing Arabic Reverse Dictionary Systems: A Transformer-Based Approach with Dataset Construction Guidelines","ref_index":2019,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B6EFG7UIBV52T6ARXOHWUFKAUP","json":"https://pith.science/pith/B6EFG7UIBV52T6ARXOHWUFKAUP.json","graph_json":"https://pith.science/api/pith-number/B6EFG7UIBV52T6ARXOHWUFKAUP/graph.json","events_json":"https://pith.science/api/pith-number/B6EFG7UIBV52T6ARXOHWUFKAUP/events.json","paper":"https://pith.science/paper/B6EFG7UI"},"agent_actions":{"view_html":"https://pith.science/pith/B6EFG7UIBV52T6ARXOHWUFKAUP","download_json":"https://pith.science/pith/B6EFG7UIBV52T6ARXOHWUFKAUP.json","view_paper":"https://pith.science/paper/B6EFG7UI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.01451&json=true","fetch_graph":"https://pith.science/api/pith-number/B6EFG7UIBV52T6ARXOHWUFKAUP/graph.json","fetch_events":"https://pith.science/api/pith-number/B6EFG7UIBV52T6ARXOHWUFKAUP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B6EFG7UIBV52T6ARXOHWUFKAUP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B6EFG7UIBV52T6ARXOHWUFKAUP/action/storage_attestation","attest_author":"https://pith.science/pith/B6EFG7UIBV52T6ARXOHWUFKAUP/action/author_attestation","sign_citation":"https://pith.science/pith/B6EFG7UIBV52T6ARXOHWUFKAUP/action/citation_signature","submit_replication":"https://pith.science/pith/B6EFG7UIBV52T6ARXOHWUFKAUP/action/replication_record"}},"created_at":"2026-05-17T23:49:34.587154+00:00","updated_at":"2026-05-17T23:49:34.587154+00:00"}