{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:XCHPCXDBGR2IGGUOQVJ7ABT7KC","short_pith_number":"pith:XCHPCXDB","canonical_record":{"source":{"id":"1706.05111","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-15T23:07:06Z","cross_cats_sorted":[],"title_canon_sha256":"6f6b658dbe6a5442455886d13b5148205310ffef66e05ed86e38d40d501149e5","abstract_canon_sha256":"10ca5fe1f04eaaf85f619fce497e75403f177e9f07780641c490d9bf1d24dbb0"},"schema_version":"1.0"},"canonical_sha256":"b88ef15c613474831a8e8553f0067f50addbb56a2ebe45cc4d5a3f85629af777","source":{"kind":"arxiv","id":"1706.05111","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05111","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05111v1","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05111","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"pith_short_12","alias_value":"XCHPCXDBGR2I","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"XCHPCXDBGR2IGGUO","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"XCHPCXDB","created_at":"2026-05-18T12:31:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:XCHPCXDBGR2IGGUOQVJ7ABT7KC","target":"record","payload":{"canonical_record":{"source":{"id":"1706.05111","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-15T23:07:06Z","cross_cats_sorted":[],"title_canon_sha256":"6f6b658dbe6a5442455886d13b5148205310ffef66e05ed86e38d40d501149e5","abstract_canon_sha256":"10ca5fe1f04eaaf85f619fce497e75403f177e9f07780641c490d9bf1d24dbb0"},"schema_version":"1.0"},"canonical_sha256":"b88ef15c613474831a8e8553f0067f50addbb56a2ebe45cc4d5a3f85629af777","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:12.663906Z","signature_b64":"NEaYENzQlbyVeqFhyX2BT/JPsUkwhfwXJqDQj1AqLBS8VCuBXN4TUBTPTu5rGJxTsHw5N9riQ92CeWUD6tO9CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b88ef15c613474831a8e8553f0067f50addbb56a2ebe45cc4d5a3f85629af777","last_reissued_at":"2026-05-18T00:38:12.663084Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:12.663084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.05111","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:38:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F+c+4Qr8ZXjSSqV2LPmGHmkvgSPqMf9A1vXQNwsHK6X0eDso3Dzu88tduJdAL2jYq64HwhkgGlS2CM+z61zkDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T19:57:40.245350Z"},"content_sha256":"ac35207cdd1c1c0d44d3b0ce44b38ffa947d3a59d9c23ba13c497b6699150826","schema_version":"1.0","event_id":"sha256:ac35207cdd1c1c0d44d3b0ce44b38ffa947d3a59d9c23ba13c497b6699150826"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:XCHPCXDBGR2IGGUOQVJ7ABT7KC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Mixture Model for Learning Multi-Sense Word Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ashutosh Modi, Dai Quoc Nguyen, Dat Quoc Nguyen, Manfred Pinkal, Stefan Thater","submitted_at":"2017-06-15T23:07:06Z","abstract_excerpt":"Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05111","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:38:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oyNhhESq1VJ56Cb+6TCsGk9Qi/5SD/ZJ8jVud8zGXVhDnfnK523ZJkTnz+h7JqByg3sWcCUeHmoLAwD8nkadCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T19:57:40.245699Z"},"content_sha256":"bc4b78bb82f30531e54e40f8292f55526982ada73ed2d82574cb3ff166fc4cb2","schema_version":"1.0","event_id":"sha256:bc4b78bb82f30531e54e40f8292f55526982ada73ed2d82574cb3ff166fc4cb2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XCHPCXDBGR2IGGUOQVJ7ABT7KC/bundle.json","state_url":"https://pith.science/pith/XCHPCXDBGR2IGGUOQVJ7ABT7KC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XCHPCXDBGR2IGGUOQVJ7ABT7KC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-19T19:57:40Z","links":{"resolver":"https://pith.science/pith/XCHPCXDBGR2IGGUOQVJ7ABT7KC","bundle":"https://pith.science/pith/XCHPCXDBGR2IGGUOQVJ7ABT7KC/bundle.json","state":"https://pith.science/pith/XCHPCXDBGR2IGGUOQVJ7ABT7KC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XCHPCXDBGR2IGGUOQVJ7ABT7KC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:XCHPCXDBGR2IGGUOQVJ7ABT7KC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"10ca5fe1f04eaaf85f619fce497e75403f177e9f07780641c490d9bf1d24dbb0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-15T23:07:06Z","title_canon_sha256":"6f6b658dbe6a5442455886d13b5148205310ffef66e05ed86e38d40d501149e5"},"schema_version":"1.0","source":{"id":"1706.05111","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05111","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05111v1","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05111","created_at":"2026-05-18T00:38:12Z"},{"alias_kind":"pith_short_12","alias_value":"XCHPCXDBGR2I","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"XCHPCXDBGR2IGGUO","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"XCHPCXDB","created_at":"2026-05-18T12:31:53Z"}],"graph_snapshots":[{"event_id":"sha256:bc4b78bb82f30531e54e40f8292f55526982ada73ed2d82574cb3ff166fc4cb2","target":"graph","created_at":"2026-05-18T00:38:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.","authors_text":"Ashutosh Modi, Dai Quoc Nguyen, Dat Quoc Nguyen, Manfred Pinkal, Stefan Thater","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-15T23:07:06Z","title":"A Mixture Model for Learning Multi-Sense Word Embeddings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05111","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ac35207cdd1c1c0d44d3b0ce44b38ffa947d3a59d9c23ba13c497b6699150826","target":"record","created_at":"2026-05-18T00:38:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"10ca5fe1f04eaaf85f619fce497e75403f177e9f07780641c490d9bf1d24dbb0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-06-15T23:07:06Z","title_canon_sha256":"6f6b658dbe6a5442455886d13b5148205310ffef66e05ed86e38d40d501149e5"},"schema_version":"1.0","source":{"id":"1706.05111","kind":"arxiv","version":1}},"canonical_sha256":"b88ef15c613474831a8e8553f0067f50addbb56a2ebe45cc4d5a3f85629af777","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b88ef15c613474831a8e8553f0067f50addbb56a2ebe45cc4d5a3f85629af777","first_computed_at":"2026-05-18T00:38:12.663084Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:12.663084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NEaYENzQlbyVeqFhyX2BT/JPsUkwhfwXJqDQj1AqLBS8VCuBXN4TUBTPTu5rGJxTsHw5N9riQ92CeWUD6tO9CQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:12.663906Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.05111","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ac35207cdd1c1c0d44d3b0ce44b38ffa947d3a59d9c23ba13c497b6699150826","sha256:bc4b78bb82f30531e54e40f8292f55526982ada73ed2d82574cb3ff166fc4cb2"],"state_sha256":"5ef319238801c4dacba9ad818d9cbdc2b800d0eff37518abe482e6de5f25acd9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kQrVA/efTNR9v1Lt32lgp+aaVZn/uigcHhlyLMp4dBYMolcrEmOqZfqbiEmDNasg70pCpw5HbwMLwTILCwLkDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-19T19:57:40.247770Z","bundle_sha256":"7fcb68713794b0895df9ef2868044effc59eadf87e9c44ee9379f838ee54a7f4"}}