{"_canonicalization":{"envelope_id":"axm_ + sha256(envelope minus {signature, axiom_id, anchors})","envelope_signature":"ed25519(envelope minus {signature, axiom_id})","json":"sort_keys=True, separators=(',',':'), ensure_ascii=False, allow_nan=False, utf-8","leaf_hash":"sha256(0x00 || canonical_json(envelope_full))","seal_signature":"ed25519(seal minus {signature, sig_algorithm})"},"axiom_id":"axm_c1f7c82bc4d5e1fbd62f4381118ae0035d6f5c73a4aca53988e8b668349b00b8","bitcoin_anchor":{"bitcoin_attestations":[],"calendar_attestations":[],"ots_url":"","stamped_at":"","status":"pending_next_stamp"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_c1f7c82bc4d5e1fbd62f4381118ae0035d6f5c73a4aca53988e8b668349b00b8","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"1d41eea63ea9063119d0b566faec182ae813579d8d22463fb69bbde2fa60de31","published":"Fri, 29 May 2026 00:00:00 -0400","receipt_hash":"1d41eea63ea9063119d0b566faec182ae813579d8d22463fb69bbde2fa60de31","schema":"spider.news.vendor_press.v1","spider":"vendor_press","spider_record":{"axiom_subtype":"news.vendor_press.v1","category":"news","decision_hint":"POSITIVE","envelope_target":"AX.OBS","fingerprint":"1d41eea63ea9063119d0b566faec182ae813579d8d22463fb69bbde2fa60de31","observed_at":"2026-05-29T04:43:58.478092Z","parent_run_hash":"0fcd87efcfe67ccb9952f747541debc16793919a4d20fd71ca0ad5516a0a13ee","published":"Fri, 29 May 2026 00:00:00 -0400","runtime_version":"0.1.0","schema":"spider.news.vendor_press.v1","source_status":200,"source_url":"https://export.arxiv.org/rss/cs.AI","spider":"vendor_press","summary_excerpt":"arXiv:2605.28834v1 Announce Type: cross \nAbstract: Syllabification describes the task of dividing words into syllables. Due to many rules and exceptions, training an algorithm to perform syllabification with high accuracy remains a challenge. Throughout the last decades, different algorithms have been put forth for Dutch syllabification, yet a comprehensive comparative assessment has not been done. Additionally, deep learning has gained significant popularity within NLP in recent years, yet no modern deep-learning based framework has been developed for Dutch orthographic syllabification. Finally, phonetic and orthographic syllabification algorithms have been examined separately, but not in combination. The aim of the current research was twofold: (a) to examine the performance of existing Dutch syllabification algorithms, and (b) to investigate whether combining phonetic and orthographic information into a single model can increase syllabification performance. To compare the performanc","title":"Assessing Dutch Syllabification Algorithms and Improving Accuracy by Combining Phonetic and Orthographic Information through Deep Learning","url":"https://arxiv.org/abs/2605.28834","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.28834v1 Announce Type: cross \nAbstract: Syllabification describes the task of dividing words into syllables. Due to many rules and exceptions, training an algorithm to perform syllabification with high accuracy remains a challenge. Throughout the last decades, different algorithms have been put forth for Dutch syllabification, yet a comprehensive comparative assessment has not been done. Additionally, deep learning has gained significant popularity within NLP in recent years, yet no modern deep-learning based framework has been developed for Dutch orthographic syllabification. Finally, phonetic and orthographic syllabification algorithms have been examined separately, but not in combination. The aim of the current research was twofold: (a) to examine the performance of existing Dutch syllabification algorithms, and (b) to investigate whether combining phonetic and orthographic information into a single model can increase syllabification performance. To compare the performanc","title":"Assessing Dutch Syllabification Algorithms and Improving Accuracy by Combining Phonetic and Orthographic Information through Deep Learning","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-29T04:43:58Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2605.28834"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:b452a48ef4075d044fe73cb26d2f72018380ee671be43a8ffeb9b5bef8eedd8440a4272f974620ddf68d67b11a2d197ff5161eb47d86a57ee5bd33baa88fa106","signer":"crovia.substrate","subject":{"observed_at":"2026-05-29T04:43:58Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2605.28834"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"57538752c856b5642bdf8e855570d6f154dc1c2859224cdaa4dabcdea9bf3cb2","leaf_index":157791,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"4c8b8672defd6fa7cfd80e6c76fa77971278c326797d6c92c2b254afcf3ace75","side":"left"},{"sibling":"9ba6edb5d058295c376703fa10b809a84c31d972de6272b8fb6d6714694f27c2","side":"left"},{"sibling":"cd2c107306a8fb7dd26568e1b42595a071b3ed89a4251ab4a203b47a424671a7","side":"left"},{"sibling":"f0d4561268d03d9d6c837af1967123202f80cd9af05a870fa2660524012022ad","side":"left"},{"sibling":"ad8b1da722b305b4304e73ba648c3b17383d5958e74a4d15a5110093856d6df3","side":"left"},{"sibling":"1a62cdbbf6beab9c4122048f06e3086d2dfd9a7569e87288f286b57cf5bc2003","side":"right"},{"sibling":"8a2f71675e192857299c484b4c2ecd6b3fe6098871f8b781ac9f3ed3449ef468","side":"left"},{"sibling":"85dd98b6b813fff84d9d7c892cf52e58d31740ddc1a693741a96ae274811f1ea","side":"right"},{"sibling":"291a37d6414d385e45486ef4725ce7087043d900d04f90b309d04bd876c338e5","side":"right"},{"sibling":"1dcc44e23fbb0218b13591e4b584eca3600dcf365769cb741e0ecd33b25b8c56","side":"right"},{"sibling":"fcf16a6f44025801f5b83e928acce764352ddbc06ae3043d8cde9a409933e6d8","side":"right"},{"sibling":"78982294dee68f9db9288c64d7e507c7865fda96e1b6f7ccff5c8bb152e93c49","side":"left"},{"sibling":"995b421824624a8282c7f44e64c64ee35344800f477ae1845b41be14d3fab94c","side":"right"},{"sibling":"66331bac84ca0f8983eb09fac7eaf95af234f1b82680b793eabff4ee25caac40","side":"left"},{"sibling":"35ca36cee447f0ef7064a25d55f59357c66901e31427729c4c1d8b14aa8adb6c","side":"left"},{"sibling":"eef0e8906a749d3470f89beeedc723f37a5737010bbb0dcc7cf91515338e5a3e","side":"right"},{"sibling":"1a07e481a9407d71aad078ce854cdeee362163c887fe10f889b0ecf0b5e749ad","side":"right"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":158251,"merkle_root":"485e6b31fe60c8beba5b394808c7e4c32448b2ff65c2482c480ca0e2a2eda718","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260529T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-29T05:37:37Z","sig_algorithm":"ed25519","signature":"bbf9f005201182fce4f9d94c7a9d01a508b56daf7d9611bd73514f5f616bc059d0e5e1f2edfc95716e6fe08ef5fae38b558cbf7f2fd8f9d5dfe4c34a54c83005","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_c1f7c82bc4d5e1fbd62f4381118ae0035d6f5c73a4aca53988e8b668349b00b8"}}