{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:K4HE5HR7X3P2FF62M5JPCSQBWQ","short_pith_number":"pith:K4HE5HR7","schema_version":"1.0","canonical_sha256":"570e4e9e3fbedfa297da6752f14a01b41f32e442ae3ae748f16c3bc5b359f545","source":{"kind":"arxiv","id":"1510.02049","version":1},"attestation_state":"computed","paper":{"title":"Assisting Composition of Email Responses: a Topic Prediction Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jean Michel Renders, Marc Dymetman, Spandana Gella, Sriram Venkatapathy","submitted_at":"2015-10-07T18:08:45Z","abstract_excerpt":"We propose an approach for helping agents compose email replies to customer requests. To enable that, we use LDA to extract latent topics from a collection of email exchanges. We then use these latent topics to label our data, obtaining a so-called \"silver standard\" topic labelling. We exploit this labelled set to train a classifier to: (i) predict the topic distribution of the entire agent's email response, based on features of the customer's email; and (ii) predict the topic distribution of the next sentence in the agent's reply, based on the customer's email features and on features of the "},"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":"1510.02049","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2015-10-07T18:08:45Z","cross_cats_sorted":[],"title_canon_sha256":"2e13aeb9d26dab0e7b90e3a6fa71839bf099d97c377c488fae7bf0b503d4cfb8","abstract_canon_sha256":"7b8b558fcc0f64a12f1fec5341b2223801e698712836caf4154c674f809dcabc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:30:49.808924Z","signature_b64":"LO9L3BUFVC8N1iD5pyRUqzIvSy/P7u9BHAcXg9mUiv8M8KUUSiKSAB94wBq+MS6SfFrPLtdKqfM1IBkDG3t8Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"570e4e9e3fbedfa297da6752f14a01b41f32e442ae3ae748f16c3bc5b359f545","last_reissued_at":"2026-05-18T01:30:49.808435Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:30:49.808435Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Assisting Composition of Email Responses: a Topic Prediction Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jean Michel Renders, Marc Dymetman, Spandana Gella, Sriram Venkatapathy","submitted_at":"2015-10-07T18:08:45Z","abstract_excerpt":"We propose an approach for helping agents compose email replies to customer requests. To enable that, we use LDA to extract latent topics from a collection of email exchanges. We then use these latent topics to label our data, obtaining a so-called \"silver standard\" topic labelling. We exploit this labelled set to train a classifier to: (i) predict the topic distribution of the entire agent's email response, based on features of the customer's email; and (ii) predict the topic distribution of the next sentence in the agent's reply, based on the customer's email features and on features of the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.02049","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":"1510.02049","created_at":"2026-05-18T01:30:49.808516+00:00"},{"alias_kind":"arxiv_version","alias_value":"1510.02049v1","created_at":"2026-05-18T01:30:49.808516+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.02049","created_at":"2026-05-18T01:30:49.808516+00:00"},{"alias_kind":"pith_short_12","alias_value":"K4HE5HR7X3P2","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_16","alias_value":"K4HE5HR7X3P2FF62","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_8","alias_value":"K4HE5HR7","created_at":"2026-05-18T12:29:27.538025+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/K4HE5HR7X3P2FF62M5JPCSQBWQ","json":"https://pith.science/pith/K4HE5HR7X3P2FF62M5JPCSQBWQ.json","graph_json":"https://pith.science/api/pith-number/K4HE5HR7X3P2FF62M5JPCSQBWQ/graph.json","events_json":"https://pith.science/api/pith-number/K4HE5HR7X3P2FF62M5JPCSQBWQ/events.json","paper":"https://pith.science/paper/K4HE5HR7"},"agent_actions":{"view_html":"https://pith.science/pith/K4HE5HR7X3P2FF62M5JPCSQBWQ","download_json":"https://pith.science/pith/K4HE5HR7X3P2FF62M5JPCSQBWQ.json","view_paper":"https://pith.science/paper/K4HE5HR7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1510.02049&json=true","fetch_graph":"https://pith.science/api/pith-number/K4HE5HR7X3P2FF62M5JPCSQBWQ/graph.json","fetch_events":"https://pith.science/api/pith-number/K4HE5HR7X3P2FF62M5JPCSQBWQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K4HE5HR7X3P2FF62M5JPCSQBWQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K4HE5HR7X3P2FF62M5JPCSQBWQ/action/storage_attestation","attest_author":"https://pith.science/pith/K4HE5HR7X3P2FF62M5JPCSQBWQ/action/author_attestation","sign_citation":"https://pith.science/pith/K4HE5HR7X3P2FF62M5JPCSQBWQ/action/citation_signature","submit_replication":"https://pith.science/pith/K4HE5HR7X3P2FF62M5JPCSQBWQ/action/replication_record"}},"created_at":"2026-05-18T01:30:49.808516+00:00","updated_at":"2026-05-18T01:30:49.808516+00:00"}