{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SNPXKSD5X5WUO2DXEJSU2E4UDW","short_pith_number":"pith:SNPXKSD5","schema_version":"1.0","canonical_sha256":"935f75487dbf6d47687722654d13941da31722f674b2b16a424dab254245243d","source":{"kind":"arxiv","id":"2502.10378","version":1},"attestation_state":"computed","paper":{"title":"Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.HC","authors_text":"Bowen Zhao, Ishan Chatterjee, Jiexin Ding, Rui Hao, Xinyun Liu, Yuanchun Shi, Yuntao Wang","submitted_at":"2025-02-14T18:57:04Z","abstract_excerpt":"English as a Second Language (ESL) learners often encounter unknown words that hinder their text comprehension. Automatically detecting these words as users read can enable computing systems to provide just-in-time definitions, synonyms, or contextual explanations, thereby helping users learn vocabulary in a natural and seamless manner. This paper presents EyeLingo, a transformer-based machine learning method that predicts the probability of unknown words based on text content and eye gaze trajectory in real time with high accuracy. A 20-participant user study revealed that our method can achi"},"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":"2502.10378","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-02-14T18:57:04Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"aedda0636830aae7a9e4da1f1f25929feeb0136cc2ff9ca787e85807be83da22","abstract_canon_sha256":"2a230189cd7011d56020518c6232cc78a05be60648c295e07aa646230b6d11d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:33.155950Z","signature_b64":"Ex64A7p9mbKjmztvDL7pA5DsWeZVNHsoMfe8FwLT1nmv9Z87m17wX0stx1GWK/bQt7Hx9hZC8+iSgWkGjUitCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"935f75487dbf6d47687722654d13941da31722f674b2b16a424dab254245243d","last_reissued_at":"2026-07-05T10:14:33.155439Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:33.155439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.HC","authors_text":"Bowen Zhao, Ishan Chatterjee, Jiexin Ding, Rui Hao, Xinyun Liu, Yuanchun Shi, Yuntao Wang","submitted_at":"2025-02-14T18:57:04Z","abstract_excerpt":"English as a Second Language (ESL) learners often encounter unknown words that hinder their text comprehension. Automatically detecting these words as users read can enable computing systems to provide just-in-time definitions, synonyms, or contextual explanations, thereby helping users learn vocabulary in a natural and seamless manner. This paper presents EyeLingo, a transformer-based machine learning method that predicts the probability of unknown words based on text content and eye gaze trajectory in real time with high accuracy. A 20-participant user study revealed that our method can achi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.10378","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.10378/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2502.10378","created_at":"2026-07-05T10:14:33.155504+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.10378v1","created_at":"2026-07-05T10:14:33.155504+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.10378","created_at":"2026-07-05T10:14:33.155504+00:00"},{"alias_kind":"pith_short_12","alias_value":"SNPXKSD5X5WU","created_at":"2026-07-05T10:14:33.155504+00:00"},{"alias_kind":"pith_short_16","alias_value":"SNPXKSD5X5WUO2DX","created_at":"2026-07-05T10:14:33.155504+00:00"},{"alias_kind":"pith_short_8","alias_value":"SNPXKSD5","created_at":"2026-07-05T10:14:33.155504+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/SNPXKSD5X5WUO2DXEJSU2E4UDW","json":"https://pith.science/pith/SNPXKSD5X5WUO2DXEJSU2E4UDW.json","graph_json":"https://pith.science/api/pith-number/SNPXKSD5X5WUO2DXEJSU2E4UDW/graph.json","events_json":"https://pith.science/api/pith-number/SNPXKSD5X5WUO2DXEJSU2E4UDW/events.json","paper":"https://pith.science/paper/SNPXKSD5"},"agent_actions":{"view_html":"https://pith.science/pith/SNPXKSD5X5WUO2DXEJSU2E4UDW","download_json":"https://pith.science/pith/SNPXKSD5X5WUO2DXEJSU2E4UDW.json","view_paper":"https://pith.science/paper/SNPXKSD5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.10378&json=true","fetch_graph":"https://pith.science/api/pith-number/SNPXKSD5X5WUO2DXEJSU2E4UDW/graph.json","fetch_events":"https://pith.science/api/pith-number/SNPXKSD5X5WUO2DXEJSU2E4UDW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SNPXKSD5X5WUO2DXEJSU2E4UDW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SNPXKSD5X5WUO2DXEJSU2E4UDW/action/storage_attestation","attest_author":"https://pith.science/pith/SNPXKSD5X5WUO2DXEJSU2E4UDW/action/author_attestation","sign_citation":"https://pith.science/pith/SNPXKSD5X5WUO2DXEJSU2E4UDW/action/citation_signature","submit_replication":"https://pith.science/pith/SNPXKSD5X5WUO2DXEJSU2E4UDW/action/replication_record"}},"created_at":"2026-07-05T10:14:33.155504+00:00","updated_at":"2026-07-05T10:14:33.155504+00:00"}