| { | |
| "paper_id": "D19-1030", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T15:56:38.434563Z" | |
| }, | |
| "title": "Cross-lingual Structure Transfer for Relation and Event Extraction", | |
| "authors": [ | |
| { | |
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| "institution": "Rensselaer Polytechnic Institute", | |
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| "institution": "Rensselaer Polytechnic Institute", | |
| "location": {} | |
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| "email": "" | |
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| "last": "Heng", | |
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| "institution": "University of Illinois at Urbana", | |
| "location": { | |
| "settlement": "Champaign" | |
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| "email": "hengji@illinois.edu" | |
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| "institution": "University of Southern California Information Sciences Institute", | |
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| "institution": "Columbia University", | |
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| "email": "" | |
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| "institution": "IBM T.J. Watson Research Center", | |
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| { | |
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| { | |
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| "email": "" | |
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| { | |
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| { | |
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| "last": "Badmaeva", | |
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| { | |
| "first": "Miguel", | |
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| "last": "Ballesteros", | |
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| { | |
| "first": "Esha", | |
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| "last": "Banerjee", | |
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| "email": "" | |
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| "email": "" | |
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| { | |
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| "Barbu" | |
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| "last": "Mititelu", | |
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| "email": "" | |
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| { | |
| "first": "Victoria", | |
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| "last": "Basmov", | |
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| "email": "" | |
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| "last": "Bellato", | |
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| "email": "" | |
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| "last": "Bengoetxea", | |
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| "email": "" | |
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| { | |
| "first": "Yevgeni", | |
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| "last": "Berzak", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Ahmad", | |
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| "last": "Bhat", | |
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| "email": "" | |
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| { | |
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| "email": "" | |
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| "first": "Erica", | |
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| "last": "Biagetti", | |
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| "email": "" | |
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| { | |
| "first": "Eckhard", | |
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| "last": "Bick", | |
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| "email": "" | |
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| { | |
| "first": "Rogier", | |
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| "last": "Blokland", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Victoria", | |
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| "last": "Bobicev", | |
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| "affiliation": {}, | |
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| { | |
| "first": "Carl", | |
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| "last": "B\u00f6rstell", | |
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| "email": "" | |
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| { | |
| "first": "Cristina", | |
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| "email": "" | |
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| { | |
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| "email": "" | |
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| { | |
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| "email": "" | |
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| "last": "Boyd", | |
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| { | |
| "first": "Aljoscha", | |
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| { | |
| "first": "Marie", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Bernard", | |
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| "last": "Caron", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Gauthier", | |
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| "last": "Caron", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Cebiro\u011flu", | |
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| "last": "Eryi\u011fit", | |
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| "email": "" | |
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| { | |
| "first": "Massimiliano", | |
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| "email": "" | |
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| { | |
| "first": "Giuseppe", | |
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| "G A" | |
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| "last": "Cecchini", | |
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| "first": "Slavom\u00edr", | |
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| "last": "Celano", | |
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| { | |
| "first": "Savas", | |
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| "last": "\u010c\u00e9pl\u00f6", | |
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| "email": "" | |
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| { | |
| "first": "Fabricio", | |
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| "last": "Cetin", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Jinho", | |
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| "last": "Chalub", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Yongseok", | |
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| "last": "Choi", | |
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| { | |
| "first": "Jayeol", | |
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| "last": "Cho", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "", | |
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| "last": "Chun", | |
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| "email": "" | |
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| { | |
| "first": "Kaja", | |
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| "last": "Dirix", | |
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| "affiliation": {}, | |
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| { | |
| "first": "Timothy", | |
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| "first": "Kira", | |
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| { | |
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| { | |
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| "first": "Rich\u00e1rd", | |
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| "last": "Etienne", | |
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| "email": "" | |
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| { | |
| "first": "Hector", | |
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| "last": "Farkas", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Al- Calde", | |
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| "last": "Fernandez", | |
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| "email": "" | |
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| "first": "Jennifer", | |
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| "last": "Foster", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Cl\u00e1udia", | |
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| "last": "Freitas", | |
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| "email": "" | |
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| "first": "Katar\u00edna", | |
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| "last": "Gajdo\u0161ov\u00e1", | |
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| { | |
| "first": "Daniel", | |
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| "last": "Galbraith", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Marcos", | |
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| "last": "Garcia", | |
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| "affiliation": {}, | |
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| { | |
| "first": "Moa", | |
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| "last": "G\u00e4rdenfors", | |
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| "email": "" | |
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| "last": "Garza", | |
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| "affiliation": {}, | |
| "email": "" | |
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| "first": "Kim", | |
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| "last": "Gerdes", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Filip", | |
| "middle": [], | |
| "last": "Ginter", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Iakes", | |
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| "last": "Goenaga", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| "last": "Gojenola", | |
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| "first": "Memduh", | |
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| "last": "G\u00f6k\u0131rmak", | |
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| "first": "Yoav", | |
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| "last": "Goldberg", | |
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| "email": "" | |
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| { | |
| "first": "Xavier", | |
| "middle": [], | |
| "last": "G\u00f3mez", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Berta", | |
| "middle": [ | |
| "Gonz\u00e1les" | |
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| "last": "Saavedra", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Matias", | |
| "middle": [], | |
| "last": "Grioni", | |
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| "last": "Guillaume", | |
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| { | |
| "first": "C\u00e9line", | |
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| "first": "Nizar", | |
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| { | |
| "first": "Jan", | |
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| "last": "Haji\u010d", | |
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| "first": "Linh", | |
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| "first": "Na-Rae", | |
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| "last": "Han", | |
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| { | |
| "first": "Kim", | |
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| "last": "Hlav\u00e1\u010dov\u00e1", | |
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| { | |
| "first": "Florinel", | |
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| { | |
| "first": "Petter", | |
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| "last": "Hohle", | |
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| { | |
| "first": "Jena", | |
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| "last": "Hwang", | |
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| "affiliation": {}, | |
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| { | |
| "first": "Radu", | |
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| "last": "Ion", | |
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| { | |
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| "last": "Irimia", | |
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| { | |
| "first": "\u1eccl\u00e1j\u00edd\u00e9", | |
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| "last": "Ishola", | |
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| { | |
| "first": "Tom\u00e1\u0161", | |
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| "last": "Jel\u00ednek", | |
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| "abstract": "The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages. We investigate the suitability of crosslingual structure transfer techniques for these tasks. We exploit relation-and event-relevant language-universal features, leveraging both symbolic (including part-of-speech and dependency path) and distributional (including type representation and contextualized representation) information. By representing all entity mentions, event triggers, and contexts into this complex and structured multilingual common space, using graph convolutional networks, we can train a relation or event extractor from source language annotations and apply it to the target language. Extensive experiments on cross-lingual relation and event transfer among English, Chinese, and Arabic demonstrate that our approach achieves performance comparable to state-of-the-art supervised models trained on up to 3,000 manually annotated mentions: up to 62.6% F-score for Relation Extraction, and 63.1% F-score for Event Argument Role Labeling. The event argument role labeling model transferred from English to Chinese achieves similar performance as the model trained from Chinese. We thus find that language-universal symbolic and distributional representations are complementary for cross-lingual structure transfer.", | |
| "pdf_parse": { | |
| "paper_id": "D19-1030", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages. We investigate the suitability of crosslingual structure transfer techniques for these tasks. We exploit relation-and event-relevant language-universal features, leveraging both symbolic (including part-of-speech and dependency path) and distributional (including type representation and contextualized representation) information. By representing all entity mentions, event triggers, and contexts into this complex and structured multilingual common space, using graph convolutional networks, we can train a relation or event extractor from source language annotations and apply it to the target language. Extensive experiments on cross-lingual relation and event transfer among English, Chinese, and Arabic demonstrate that our approach achieves performance comparable to state-of-the-art supervised models trained on up to 3,000 manually annotated mentions: up to 62.6% F-score for Relation Extraction, and 63.1% F-score for Event Argument Role Labeling. The event argument role labeling model transferred from English to Chinese achieves similar performance as the model trained from Chinese. We thus find that language-universal symbolic and distributional representations are complementary for cross-lingual structure transfer.", | |
| "cite_spans": [], | |
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| "section": "Abstract", | |
| "sec_num": null | |
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| ], | |
| "body_text": [ | |
| { | |
| "text": "Advanced Information Extraction (IE) tasks entail predicting structures, such as relations between entities, and events involving entities. Given a pair of entity mentions, Relation Extraction aims to identify the relation between the mentions and classify it by predefined type. Event Extraction aims to identify event triggers and their arguments in unstructured texts and classify them respectively by predefined types and roles. As Figure 1 illustrates, both tasks entail predicting an information network for each sentence, where the entity mentions and event triggers are nodes, and the relations and event-argument links are edges labeled with their relation and argument roles, respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 436, | |
| "end": 444, | |
| "text": "Figure 1", | |
| "ref_id": "FIGREF0" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "There are certain relations and events that are of primary interest to a given community and so are reported predominantly in the low-resource language data sources available to that community. For example, though English language news will occasionally discuss the Person Aung San Suu Kyi, the vast majority of Physical-Located relations and Meeting events involving her are only reported locally in Burmese news, and thus, without Burmese relation and event extraction, a knowledge graph of this person will lack this available information. Unfortunately, publiclyavailable gold-standard annotations for relation and event extraction exist for only a few languages (Doddington et al., 2004; Getman et al., 2018) , and Burmese is not among them. Compared to other IE tasks such as name tagging, the annotations for Relation and Event Extraction are also more costly to obtain, because they are structured and require a rich label space.", | |
| "cite_spans": [ | |
| { | |
| "start": 667, | |
| "end": 692, | |
| "text": "(Doddington et al., 2004;", | |
| "ref_id": "BIBREF10" | |
| }, | |
| { | |
| "start": 693, | |
| "end": 713, | |
| "text": "Getman et al., 2018)", | |
| "ref_id": "BIBREF14" | |
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| ], | |
| "ref_spans": [], | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Recent research (Lin et al., 2017) has found that relational facts are typically expressed by identifiable patterns within languages and has shown ", | |
| "cite_spans": [ | |
| { | |
| "start": 16, | |
| "end": 34, | |
| "text": "(Lin et al., 2017)", | |
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| "sec_num": "1" | |
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| { | |
| "text": "The detainees were taken to a processing center dependency embedding that the consistency in patterns observed across languages can be used to improve relation extraction. Inspired by their results, we exploit languageuniversal features relevant to relation and event argument identification and classification, by way of both symbolic and distributional representations. For example, language-universal POS tagging and universal dependency parsing is available for 76 languages (Nivre et al., 2018) , entity extraction is available for 282 languages (Pan et al., 2017) , and multi-lingual word embeddings are available for 44 languages (Bojanowski et al., 2017; Joulin et al., 2018) . As shown in Figure 2 , even for distinct pairs of entity mentions (colored pink and blue, in both English and Russian), the structures share similar language-universal symbolic features, such as a common labeled depen-dency path, as well as distributional features, such as multilingual word embeddings.", | |
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| "text": "(Pan et al., 2017)", | |
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| "text": "Joulin et al., 2018)", | |
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| "section": "PHYS-LOCATED (relation)", | |
| "sec_num": null | |
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| { | |
| "text": "Based on these language-universal representations, we then project all entity mentions, event triggers and their contexts into one multilingual common space. Unlike recent work on multilingual common space construction that makes use of linear mappings (Mikolov et al., 2013; Rothe et al., 2016; Zhang et al., 2016; Lazaridou et al., 2015; Xing et al., 2015; Smith et al., 2017) or canonical correlation analysis (CCA) (Ammar et al., 2016; Faruqui and Dyer, 2014; Lu et al., 2015) to transfer surface features across languages, our major innovation is to convert the text data into structured representations derived from universal dependency parses and enhanced with distributional information to capture individual entities as well as the relations and events involving those entities, so we can share structural representations across multiple languages.", | |
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| "text": "Faruqui and Dyer, 2014;", | |
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| "text": "Then we construct a novel cross-lingual structure transfer learning framework to project source language (SL) training data and target language (TL) test data into the common semantic space, so that we can train a relation or event extractor from SL annotations and apply the resulting extractor to TL texts. We adopt graph convolutional networks (GCN) (Kipf and Welling, 2017; Marcheggiani and Titov, 2017) to encode graph structures over the input data, applying graph convolution operations to generate entity and word representations in a latent space. In contrast to other encoders such a Tree-LSTM (Tai et al., 2015) , GCN can cover more complete contextual information from dependency parses because, for each word, it captures all parse tree neighbors of the word, rather than just the child nodes of the word. Using this shared encoder, we treat the two tasks of relation extraction and event argument role labeling as mappings from the latent space to relation type and to event type and argument role, respectively.", | |
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| "text": "Extensive experiments on cross-lingual relation and event transfer among English, Chinese, and Arabic show that our approach achieves highly promising performance on both tasks.", | |
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| { | |
| "text": "Our cross-lingual structure transfer approach (see Figure 2 ) consists of four steps: (1) Convert each sentence in any language into a language-universal tree structure based on universal dependency parsing. (2) For each node in the tree structure, create a representation from the concatenation of multilingual word embedding, language-universal POS embedding, dependency role embedding and entity-type embedding, so that all sentences, independent of their language, are represented within one shared semantic space. (3) Adopt GCN to generate contextualized word representations by leveraging information from neighbors derived from the dependency parsing tree, and (4) Using this shared semantic space, train relation and event argument extractors with high-resource language training data, and apply the resulting extractors to texts of low-resource languages that do not have any relation or event argument annotations.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 51, | |
| "end": 59, | |
| "text": "Figure 2", | |
| "ref_id": "FIGREF2" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Overview", | |
| "sec_num": "2.1" | |
| }, | |
| { | |
| "text": "Most previous approaches regard a sentence as a linear sequence of words, and incorporate language-specific information such as word order. Unlike sequence representations, tree representations such as constituency trees and dependency trees are typically constructed following a combination of syntactic principles and annotation guidelines designed by linguists. The resulting structures, such as the verbsubject relation and the verbobject relation, are found across languages. In this paper, we choose dependency trees as the sentence representations because the community has made great efforts at developing language-universal dependency parsing resources across 83 languages (Nivre et al., 2016) .", | |
| "cite_spans": [ | |
| { | |
| "start": 682, | |
| "end": 702, | |
| "text": "(Nivre et al., 2016)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Tree Representations", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "We define the dependency-based tree representation for a sentence as", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Tree Representations", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "G = (V, E), where V = {v 1 , v 2 , ..., v N }", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Tree Representations", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "is a set of words, and E = {e 1 , e 2 , ..., e M } is a set of language-universal syntactic relations. N is the number of words in the sentence and M is the number of dependency relations between words. To make this tree representation language-universal, we first convert each tree node into a vector which is a concatenation of three language-universal representations at wordlevel: multilingual word embedding, POS embedding (Nivre et al., 2016), entity-type embedding, and dependency relation embedding. More details are reported in Section 3.2.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Tree Representations", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "Structural information is important for relation extraction and event argument role labeling, thus we aim to generate contextualized word representations by leveraging neighbors in dependency trees for each node.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "GCN Encoder", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "Our GCN encoder is based on the monolingual design by Zhang et al. (2018b) . The graphical sentence representation obtained from dependency parsing of a sentence with N tokens is converted into an N \u00d7 N adjacency matrix A, with added self-connections at each node to help capture information about the current node itself, as in Kipf and Welling (2017). Here, A i,j = 1 denotes the presence of a directed edge from node i to node j in the dependency tree. Initially, each node contains distributional information about the i th word, including word embedding x w i , embeddings for symbolic information including its POS tag x p i , dependency relation x d i and entity type x e i .", | |
| "cite_spans": [ | |
| { | |
| "start": 54, | |
| "end": 74, | |
| "text": "Zhang et al. (2018b)", | |
| "ref_id": "BIBREF71" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "GCN Encoder", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "We represent this initial representation h 0 i as follows:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "GCN Encoder", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "h (0) i = x w i \u2295 x p i \u2295 x d i \u2295 x e i", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "GCN Encoder", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "Then, at the k th layer of convolution, the hidden representation is derived from the representations of its neighbors at the (k \u2212 1) th layer. Thus, the hidden representation at the k th layer for the i th node is computed as:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "GCN Encoder", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "h (k) i = ReLU ( N \u2211 j=0 A ij W (k) h (k\u22121) j d i + b (k) )", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "GCN Encoder", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "where W is the weight vector, b is the bias term, and d i represents the degree of the i th node. The denominator in the equation denotes the normalization factor to neutralize the negative impact of node degree (Zhang et al., 2018b) . The final hidden representation of each node after the k th layer is the encoding of each word h (k) i in our language universal common space, and incorporates information about neighbors up to k hops away in the dependency tree.", | |
| "cite_spans": [ | |
| { | |
| "start": 212, | |
| "end": 233, | |
| "text": "(Zhang et al., 2018b)", | |
| "ref_id": "BIBREF71" | |
| }, | |
| { | |
| "start": 333, | |
| "end": 336, | |
| "text": "(k)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "GCN Encoder", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "The GCN encoder generates the final hidden representation, h (k) i , for each of the n nodes. We perform max-pooling over these final node representations to obtain a single vector representation for the sentence, h s . Similar to previous work (Zhang et al., 2018b) , we also use the following recipe to obtain relation type classification results for each mention pair in a sentence: (1) max-pooling over the final representations of the nodes representing entity mentions, to get a single vector representation for each mention in a pair under consideration, h m 1 and h m 2 , (2) a concatenation of three results of max-pooling:", | |
| "cite_spans": [ | |
| { | |
| "start": 61, | |
| "end": 64, | |
| "text": "(k)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 245, | |
| "end": 266, | |
| "text": "(Zhang et al., 2018b)", | |
| "ref_id": "BIBREF71" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Application on Relation Extraction", | |
| "sec_num": "2.4" | |
| }, | |
| { | |
| "text": "([h m 1 ; h s ; h m 2 ])", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Application on Relation Extraction", | |
| "sec_num": "2.4" | |
| }, | |
| { | |
| "text": "to combine contextual sentence information with entity mention information (Santoro et al., 2017; Lee et al., 2017; Zhang et al., 2018b) , (3) a linear layer to generate a combined representation of these concatenated results, and (4) a Softmax output layer for relation type classification. The objective function used here is as follows:", | |
| "cite_spans": [ | |
| { | |
| "start": 75, | |
| "end": 97, | |
| "text": "(Santoro et al., 2017;", | |
| "ref_id": "BIBREF55" | |
| }, | |
| { | |
| "start": 98, | |
| "end": 115, | |
| "text": "Lee et al., 2017;", | |
| "ref_id": "BIBREF23" | |
| }, | |
| { | |
| "start": 116, | |
| "end": 136, | |
| "text": "Zhang et al., 2018b)", | |
| "ref_id": "BIBREF71" | |
| } | |
| ], | |
| "ref_spans": [], | |
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| "section": "Application on Relation Extraction", | |
| "sec_num": "2.4" | |
| }, | |
| { | |
| "text": "EQUATION", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [ | |
| { | |
| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "L r = N \u2211 i=1 L i \u2211 j=1 y ij log(\u03c3(U r \u2022 [h m1 i ; h s ij ; h m2 j ]))", | |
| "eq_num": "(1)" | |
| } | |
| ], | |
| "section": "Application on Relation Extraction", | |
| "sec_num": "2.4" | |
| }, | |
| { | |
| "text": "where U r is a weight matrix.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Application on Relation Extraction", | |
| "sec_num": "2.4" | |
| }, | |
| { | |
| "text": "Event argument role labeling distinguishes arguments from non-arguments and classifies arguments by argument role. To label the role of an argument candidate (x a j ) for an event trigger (x t i ), we first generate the sentence representation h s , argument candidate representation h a , and trigger representation h t by applying pooling on sentence, argument candidate x a j and event trigger x t i respectively, which is the same as that for relation extraction. The mapping function from the latent space to argument roles is composed of a concatenation operation ([h t ; h s ; h a ]), a linear layer (U a ) and a Softmax output layer:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Application on Event Argument Role Labeling", | |
| "sec_num": "2.5" | |
| }, | |
| { | |
| "text": "L a = N \u2211 i=1 L i \u2211 j=1 y ij log(\u03c3(U a \u2022 [h t i ; h s ij ; h a j ])) (2)", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Application on Event Argument Role Labeling", | |
| "sec_num": "2.5" | |
| }, | |
| { | |
| "text": "where N is the number of event mentions, L i is the number of argument candidates for i th event mention and \u03c3 is the Sigmoid function. We choose the Automatic Content Extraction (ACE) 2005 corpus (Walker et al., 2006) for our experiments because it contains the most comprehensive gold-standard relation and event annotations for three distinct languages: English, Chinese and Arabic (see Table 1 ). Our target ontology includes the 7 entity types, 18 relation subtypes and 33 event subtypes defined in ACE. We randomly choose 80% of the corpus for training, 10% for development and 10% for blind test. We downsample the negative training instances by limiting the number of negative samples to be no more than the number of positive samples for each document.", | |
| "cite_spans": [ | |
| { | |
| "start": 197, | |
| "end": 218, | |
| "text": "(Walker et al., 2006)", | |
| "ref_id": "BIBREF60" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 390, | |
| "end": 397, | |
| "text": "Table 1", | |
| "ref_id": "TABREF1" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Application on Event Argument Role Labeling", | |
| "sec_num": "2.5" | |
| }, | |
| { | |
| "text": "For data preprocessing, we apply the Stanford CoreNLP toolkit for Chinese word segmentation and English tokenization, and the API provided by UDPipe (Straka and Strakov\u00e1, 2017) for Arabic tokenization. We use UDPipe 1 for POS tagging and dependency parsing for all three languages.", | |
| "cite_spans": [ | |
| { | |
| "start": 149, | |
| "end": 176, | |
| "text": "(Straka and Strakov\u00e1, 2017)", | |
| "ref_id": "BIBREF57" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Data and Evaluation Metrics", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "We follow the following criteria in previous work (Ji and Grishman, 2008; for evaluation:", | |
| "cite_spans": [ | |
| { | |
| "start": 50, | |
| "end": 73, | |
| "text": "(Ji and Grishman, 2008;", | |
| "ref_id": "BIBREF17" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Data and Evaluation Metrics", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "\u2022 A relation mention is considered correct if its relation type is correct, and the head offsets of the two related entity mention arguments are both correct.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Data and Evaluation Metrics", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "\u2022 An event argument is correctly labeled if its event type, offsets, and role label match any of the reference argument mentions.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Data and Evaluation Metrics", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "We use the multilingual word embeddings released by (Joulin et al., 2018) which are based on aligning monolingual embeddings learned by fastText (Bojanowski et al., 2017) from Wikipedia. We adopt the universal POS tag set (17 word categories) and 37 categories of dependency relations defined by the Universal Dependencies program (Nivre et al., 2016), and seven entity types defined in ACE: person, geopolitical entity, organization, facility, location, weapon and vehicle. ", | |
| "cite_spans": [ | |
| { | |
| "start": 52, | |
| "end": 73, | |
| "text": "(Joulin et al., 2018)", | |
| "ref_id": "BIBREF18" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Training Details", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "In order to fully analyze the cross-lingual learning capabilities of our framework, we assess its performance by applying models trained with various combinations of training and test data from these three languages, as shown in Tables 3 and 4 . We can see that the results are promising. For both tasks, the models trained from English are best, followed by Chinese, and then Arabic. We find that extraction task performance degrades as the accuracy of language-dependent tools (for sentence segmentation, POS tagging, dependency parsing) degrades.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 229, | |
| "end": 244, | |
| "text": "Tables 3 and 4", | |
| "ref_id": "TABREF5" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Overall Performance", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "Using English as training data, our cross-lingual transfer approach achieves similar performance on Chinese event argument role labeling (59.0%), compared to the model trained from Chinese annotations (59.3%), which is much higher than the best reported English-to-Chinese transfer result on event argument role labeling (47.7%) (Hsi et al., 2016) .", | |
| "cite_spans": [ | |
| { | |
| "start": 329, | |
| "end": 347, | |
| "text": "(Hsi et al., 2016)", | |
| "ref_id": "BIBREF15" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Overall Performance", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "We also show polyglot results for event argument role labeling in Table 4 , by combing the training data from multiple languages. We observe that our model benefits from the combination of training data of multiple languages. The polyglot transfer learning does not provide further gains to relation extraction because the model converges quickly on a small amount of training data.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 66, | |
| "end": 73, | |
| "text": "Table 4", | |
| "ref_id": "TABREF6" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Overall Performance", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "Test ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Train", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Tables 5 and 6 show the impact of each feature category. We can see that all categories help except Chinese POS features for Relation Extraction and Arabic POS features for Event Argument Role Labeling. Many Chinese word segmentation errors occur on tokens involved in names. In Arabic, sometimes one word is a combination of Noun and Verb. For example, the single word \u202b\"\ufe8d\ufedf\ufe88\ufeb3\ufeae\ufe8d\ufe8b\ufef4\ufee0\ufef2\"\u202c means \"Israeli conflict\" in English, including both a trigger and an argument, which are not separated by our tokenizer. In contrast, two entity mentions are unlikely to be combined into one word in Arabic, thus Relation Extraction does not suffer from tokenization errors and corresponding POS features. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Ablation Study", | |
| "sec_num": "3.4" | |
| }, | |
| { | |
| "text": "We also compare the results with supervised monolingual models trained from manual annotations in the same language. Figures 3 and 4 show the learning curves of these supervised models. For event argument role labeling, we can see that without using any annotations for the target language, our approach achieves performance comparable to the supervised models trained from more than 3,000 manually annotated event argument mentions, which equal to approximately 1,326 Chinese sentences and 1,141 Arabic sentences based on the statistics of ACE data. Our model performs particularly well on relation types or argument roles that require deep understanding of wide contexts involving complex syntactic and semantic structures, such as PART-WHOLE:Artifact, PART-WHOLE:Geographical, GEN-AFF:Org-Location and ORG-AFF:Employment relations, and Injure: Victim argument role. Despite only having 14 training instances, our model achieves near 100% F-score on PART-WHOLE:Artifact relations when transferred from English to Chinese. Our model achieves 86% F1 score on PART-WHOLE:Geographical relations when transferred from English to Arabic, and 73% and 79% F1 scores on GEN-AFF:Org-Location relations when transferred from English to Chinese and to Arabic, respectively. In an Injure event, a Person can either be an Agent or a Victim. Surface lexical embedding features are often not sufficient to disambiguate them. Our model is effective at transferring structural information such as dependency relations between words, and obtains 72.97% F1 score on labeling Injure: Victim when transferred from English to Chinese, and 75.43 % from English to Arabic.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 117, | |
| "end": 132, | |
| "text": "Figures 3 and 4", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Comparison with Supervised Approach", | |
| "sec_num": "3.5" | |
| }, | |
| { | |
| "text": "In addition, our model achieves very high performance on event argument roles for which entity type is a strong indicator. For example, a weapon is much more likely to play as an Instrument rather than a Target in an Attack. Our model achieves 89.9% F1 score on Attack: Instrument and 91.4% F1 score on Personnel: POSITION argument roles when transferred from English to Chinese.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Comparison with Supervised Approach", | |
| "sec_num": "3.5" | |
| }, | |
| { | |
| "text": "Target Language F1 score Chinese 56.9 Arabic 60.1 Table 7 : Event Argument Role Labeling results (F1 %) on Chinese and Arabic using English as training data (with system generated entity mentions) Table 7 shows the results of event argument role labeling on Chinese and Arabic entity mentions automatically extracted by Stanford CoreNLP instead of manually annotated mentions. The system extracted entity mentions introduce noise and thus decrease the performance of the model, but the overall results are still promising.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 50, | |
| "end": 57, | |
| "text": "Table 7", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 197, | |
| "end": 204, | |
| "text": "Table 7", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Using System Extracted Name Mentions", | |
| "sec_num": "3.6" | |
| }, | |
| { | |
| "text": "A large number of supervised machine learning techniques have been used for English event extraction, including traditional techniques based on symbolic features (Ji and Grishman, 2008; Liao and Grishman, 2011) , joint inference models Yang and Mitchell, 2016) , and recently with neural networks (Nguyen and Grishman, 2015a; Nguyen et al., 2016; Chen et al., 2015; Nguyen and Grishman, 2018; Lu and Nguyen, 2018; Liu et al., 2018a; Zhang et al., , 2019 . English relation extraction in the early days also followed supervised paradigms Zeng et al., 2014; Nguyen and Grishman, 2015b; Miwa and Bansal, 2016; Pawar et al., 2017; Bekoulis et al., 2018; Wang et al., 2018b) . Recent efforts have attempted to reduce annotation costs using distant supervision (Mintz et al., 2009; Surdeanu et al., 2012; Min et al., 2013; Angeli et al., 2014; Quirk and Poon, 2017; Qin et al., 2018; Wang et al., 2018a) through knowledge bases (KBs), where entities and static relations are plentiful. Distant supervision is less applicable for the task of event extraction because very few dynamic events are included in KBs. These approaches, however, incorporate language-specific characteristics and thus are costly in requiring substantial amount of annotations to adapt to a new language (Chen and Vincent, 2012; Blessing and Sch\u00fctze, 2012; Li et al., 2012; Danilova et al., 2014; Agerri et al., 2016; Hsi et al., 2016; Feng et al., 2016) .", | |
| "cite_spans": [ | |
| { | |
| "start": 162, | |
| "end": 185, | |
| "text": "(Ji and Grishman, 2008;", | |
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| }, | |
| { | |
| "start": 186, | |
| "end": 210, | |
| "text": "Liao and Grishman, 2011)", | |
| "ref_id": "BIBREF28" | |
| }, | |
| { | |
| "start": 236, | |
| "end": 260, | |
| "text": "Yang and Mitchell, 2016)", | |
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| "end": 325, | |
| "text": "(Nguyen and Grishman, 2015a;", | |
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| "end": 365, | |
| "text": "Chen et al., 2015;", | |
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| "start": 366, | |
| "end": 392, | |
| "text": "Nguyen and Grishman, 2018;", | |
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| "text": "Lu and Nguyen, 2018;", | |
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| }, | |
| { | |
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| "end": 432, | |
| "text": "Liu et al., 2018a;", | |
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| { | |
| "start": 433, | |
| "end": 453, | |
| "text": "Zhang et al., , 2019", | |
| "ref_id": "BIBREF69" | |
| }, | |
| { | |
| "start": 537, | |
| "end": 555, | |
| "text": "Zeng et al., 2014;", | |
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| }, | |
| { | |
| "start": 556, | |
| "end": 583, | |
| "text": "Nguyen and Grishman, 2015b;", | |
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| { | |
| "start": 584, | |
| "end": 606, | |
| "text": "Miwa and Bansal, 2016;", | |
| "ref_id": "BIBREF42" | |
| }, | |
| { | |
| "start": 607, | |
| "end": 626, | |
| "text": "Pawar et al., 2017;", | |
| "ref_id": "BIBREF48" | |
| }, | |
| { | |
| "start": 627, | |
| "end": 649, | |
| "text": "Bekoulis et al., 2018;", | |
| "ref_id": "BIBREF3" | |
| }, | |
| { | |
| "start": 650, | |
| "end": 669, | |
| "text": "Wang et al., 2018b)", | |
| "ref_id": "BIBREF62" | |
| }, | |
| { | |
| "start": 755, | |
| "end": 775, | |
| "text": "(Mintz et al., 2009;", | |
| "ref_id": "BIBREF41" | |
| }, | |
| { | |
| "start": 776, | |
| "end": 798, | |
| "text": "Surdeanu et al., 2012;", | |
| "ref_id": "BIBREF58" | |
| }, | |
| { | |
| "start": 799, | |
| "end": 816, | |
| "text": "Min et al., 2013;", | |
| "ref_id": "BIBREF40" | |
| }, | |
| { | |
| "start": 817, | |
| "end": 837, | |
| "text": "Angeli et al., 2014;", | |
| "ref_id": "BIBREF2" | |
| }, | |
| { | |
| "start": 838, | |
| "end": 859, | |
| "text": "Quirk and Poon, 2017;", | |
| "ref_id": "BIBREF53" | |
| }, | |
| { | |
| "start": 860, | |
| "end": 877, | |
| "text": "Qin et al., 2018;", | |
| "ref_id": "BIBREF52" | |
| }, | |
| { | |
| "start": 878, | |
| "end": 897, | |
| "text": "Wang et al., 2018a)", | |
| "ref_id": "BIBREF61" | |
| }, | |
| { | |
| "start": 1272, | |
| "end": 1296, | |
| "text": "(Chen and Vincent, 2012;", | |
| "ref_id": "BIBREF6" | |
| }, | |
| { | |
| "start": 1297, | |
| "end": 1324, | |
| "text": "Blessing and Sch\u00fctze, 2012;", | |
| "ref_id": "BIBREF4" | |
| }, | |
| { | |
| "start": 1325, | |
| "end": 1341, | |
| "text": "Li et al., 2012;", | |
| "ref_id": "BIBREF24" | |
| }, | |
| { | |
| "start": 1342, | |
| "end": 1364, | |
| "text": "Danilova et al., 2014;", | |
| "ref_id": "BIBREF9" | |
| }, | |
| { | |
| "start": 1365, | |
| "end": 1385, | |
| "text": "Agerri et al., 2016;", | |
| "ref_id": "BIBREF0" | |
| }, | |
| { | |
| "start": 1386, | |
| "end": 1403, | |
| "text": "Hsi et al., 2016;", | |
| "ref_id": "BIBREF15" | |
| }, | |
| { | |
| "start": 1404, | |
| "end": 1422, | |
| "text": "Feng et al., 2016)", | |
| "ref_id": "BIBREF13" | |
| } | |
| ], | |
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| "sec_num": "4" | |
| }, | |
| { | |
| "text": "Regardless of the recent successes in applying cross-lingual transfer learning to sequence labeling tasks, such as name tagging (e.g., (Mayhew et al., 2017; Lin et al., 2018; Huang et al., 2019) ), only limited work has explored cross-lingual relation and event structure transfer. Most previous efforts working with cross-lingual structure trans-fer rely on bilingual dictionaries (Hsi et al., 2016) , parallel data (Chen and Ji, 2009; Kim et al., 2010; Qian et al., 2014) or machine translation (Faruqui and Kumar, 2015; Zou et al., 2018) . Recent methods (Lin et al., 2017; Wang et al., 2018b) aggregate consistent patterns and complementary information across languages to enhance Relation Extraction, but they do so exploiting only distributional representations.", | |
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| "start": 135, | |
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| "text": "(Mayhew et al., 2017;", | |
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| }, | |
| { | |
| "start": 157, | |
| "end": 174, | |
| "text": "Lin et al., 2018;", | |
| "ref_id": "BIBREF30" | |
| }, | |
| { | |
| "start": 175, | |
| "end": 194, | |
| "text": "Huang et al., 2019)", | |
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| }, | |
| { | |
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| "end": 400, | |
| "text": "(Hsi et al., 2016)", | |
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| { | |
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| "end": 436, | |
| "text": "(Chen and Ji, 2009;", | |
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| }, | |
| { | |
| "start": 437, | |
| "end": 454, | |
| "text": "Kim et al., 2010;", | |
| "ref_id": "BIBREF19" | |
| }, | |
| { | |
| "start": 455, | |
| "end": 473, | |
| "text": "Qian et al., 2014)", | |
| "ref_id": "BIBREF51" | |
| }, | |
| { | |
| "start": 497, | |
| "end": 522, | |
| "text": "(Faruqui and Kumar, 2015;", | |
| "ref_id": "BIBREF12" | |
| }, | |
| { | |
| "start": 523, | |
| "end": 540, | |
| "text": "Zou et al., 2018)", | |
| "ref_id": "BIBREF72" | |
| }, | |
| { | |
| "start": 558, | |
| "end": 576, | |
| "text": "(Lin et al., 2017;", | |
| "ref_id": "BIBREF29" | |
| }, | |
| { | |
| "start": 577, | |
| "end": 596, | |
| "text": "Wang et al., 2018b)", | |
| "ref_id": "BIBREF62" | |
| } | |
| ], | |
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| "sec_num": "4" | |
| }, | |
| { | |
| "text": "Event extraction shares with Semantic Role Labeling (SRL) the task of assigning to each event argument its event role label, in the process of completing other tasks to extract the full event structure (assigning event types to predicates and more specific roles to arguments). Cross-lingual transfer has been very successful for SRL. Early attempts relied on word alignment (Van der Plas et al., 2011) or bilingual dictionaries (Kozhevnikov and Titov, 2013) . Recent work incorporates universal dependencies (Praz\u00e1k and Konop\u00edk, 2017) or multilingual word embeddings for Polyglot SRL (Mulcaire et al., 2018) . Liu et al. (2019) and Mulcaire et al. (2019) exploit multi-lingual contextualized word embedding for SRL and other Polyglot NLP tasks including dependency parsing and name tagging. To the best of our knowledge, our work is the first to construct a cross-lingual structure transfer framework that combines language-universal symbolic representations and distributional representations for relation and event extraction over texts written in a language without any training data.", | |
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| { | |
| "start": 375, | |
| "end": 402, | |
| "text": "(Van der Plas et al., 2011)", | |
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| }, | |
| { | |
| "start": 429, | |
| "end": 458, | |
| "text": "(Kozhevnikov and Titov, 2013)", | |
| "ref_id": "BIBREF21" | |
| }, | |
| { | |
| "start": 509, | |
| "end": 535, | |
| "text": "(Praz\u00e1k and Konop\u00edk, 2017)", | |
| "ref_id": "BIBREF50" | |
| }, | |
| { | |
| "start": 585, | |
| "end": 608, | |
| "text": "(Mulcaire et al., 2018)", | |
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| "start": 611, | |
| "end": 628, | |
| "text": "Liu et al. (2019)", | |
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| }, | |
| { | |
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| "text": "Mulcaire et al. (2019)", | |
| "ref_id": "BIBREF43" | |
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| ], | |
| "ref_spans": [], | |
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| "section": "Related Work", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "GCN has been successfully applied to several individual monolingual NLP tasks, including relation extraction (Zhang et al., 2018b) , event detection (Nguyen and Grishman, 2018), SRL (Marcheggiani and Titov, 2017) and sentence classification (Yao et al., 2019) . We apply GCN to construct multi-lingual structural representations for cross-lingual transfer learning.", | |
| "cite_spans": [ | |
| { | |
| "start": 109, | |
| "end": 130, | |
| "text": "(Zhang et al., 2018b)", | |
| "ref_id": "BIBREF71" | |
| }, | |
| { | |
| "start": 182, | |
| "end": 212, | |
| "text": "(Marcheggiani and Titov, 2017)", | |
| "ref_id": "BIBREF37" | |
| }, | |
| { | |
| "start": 241, | |
| "end": 259, | |
| "text": "(Yao et al., 2019)", | |
| "ref_id": "BIBREF65" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Related Work", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "We show how cross-lingual relation and event argument structural representations may be transferred between languages without any training data for the target language, and conclude that language-universal symbolic and distributional representations are complementary for crosslingual structure transfer. In the future we will explore more language-universal representations such as visual features from topically-related images and videos and external background knowl-edge.", | |
| "cite_spans": [], | |
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| "section": "Conclusions and Future Work", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "This research is based upon work supported in part by U.S. DARPA LORELEI Program HR0011-15-C-0115, the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract FA8650-17-C-9116, and ARL NS-CTA No. W911NF-09-2-0053. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.", | |
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| "section": "Acknowledgement", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Universal Dependencies 2.3 models: english-ewtud-2.3-181115.udpipe, chinese-gsd-ud-2.3-181115.udpipe, arabic-padt-ud-2.3-181115.udpipe", | |
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| "bib_entries": { | |
| "BIBREF0": { | |
| "ref_id": "b0", | |
| "title": "Multilingual event detection using the newsreader pipelines", | |
| "authors": [ | |
| { | |
| "first": "Rodrigo", | |
| "middle": [], | |
| "last": "Agerri", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Egoitz", | |
| "middle": [], | |
| "last": "Itziar Aldabe", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "German", | |
| "middle": [ | |
| "Rigau" | |
| ], | |
| "last": "Laparra", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Antske", | |
| "middle": [], | |
| "last": "Claramunt", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Paul", | |
| "middle": [], | |
| "last": "Fokkens", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Rub\u00e9n", | |
| "middle": [ | |
| "Izquierdo" | |
| ], | |
| "last": "Huijgen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Marieke", | |
| "middle": [], | |
| "last": "Bevi\u00e1", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Piek", | |
| "middle": [], | |
| "last": "Van Erp", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Anne-Lyse", | |
| "middle": [], | |
| "last": "Vossen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Minard", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "Proceedings of International Conference on Language Resources and Evaluation", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Rodrigo Agerri, Itziar Aldabe, Egoitz Laparra, Ger- man Rigau Claramunt, Antske Fokkens, Paul Hui- jgen, Rub\u00e9n Izquierdo Bevi\u00e1, Marieke van Erp, Piek Vossen, Anne-Lyse Minard, et al. 2016. Multilin- gual event detection using the newsreader pipelines. In Proceedings of International Conference on Lan- guage Resources and Evaluation 2016.", | |
| "links": null | |
| }, | |
| "BIBREF1": { | |
| "ref_id": "b1", | |
| "title": "Massively multilingual word embeddings", | |
| "authors": [ | |
| { | |
| "first": "Waleed", | |
| "middle": [], | |
| "last": "Ammar", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "George", | |
| "middle": [], | |
| "last": "Mulcaire", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yulia", | |
| "middle": [], | |
| "last": "Tsvetkov", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Guillaume", | |
| "middle": [], | |
| "last": "Lample", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Chris", | |
| "middle": [], | |
| "last": "Dyer", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Noah A", | |
| "middle": [], | |
| "last": "Smith", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": { | |
| "arXiv": [ | |
| "arXiv:1602.01925" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Waleed Ammar, George Mulcaire, Yulia Tsvetkov, Guillaume Lample, Chris Dyer, and Noah A Smith. 2016. Massively multilingual word embeddings. arXiv preprint arXiv:1602.01925.", | |
| "links": null | |
| }, | |
| "BIBREF2": { | |
| "ref_id": "b2", | |
| "title": "Combining distant and partial supervision for relation extraction", | |
| "authors": [ | |
| { | |
| "first": "Gabor", | |
| "middle": [], | |
| "last": "Angeli", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Julie", | |
| "middle": [], | |
| "last": "Tibshirani", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jean", | |
| "middle": [], | |
| "last": "Wu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Christopher D", | |
| "middle": [], | |
| "last": "Manning", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Gabor Angeli, Julie Tibshirani, Jean Wu, and Christo- pher D Manning. 2014. Combining distant and par- tial supervision for relation extraction. In Proceed- ings of the 2014 Conference on Empirical Methods in Natural Language Processing.", | |
| "links": null | |
| }, | |
| "BIBREF3": { | |
| "ref_id": "b3", | |
| "title": "Adversarial training for multi-context joint entity and relation extraction", | |
| "authors": [ | |
| { | |
| "first": "Giannis", | |
| "middle": [], | |
| "last": "Bekoulis", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Johannes", | |
| "middle": [], | |
| "last": "Deleu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Thomas", | |
| "middle": [], | |
| "last": "Demeester", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Chris", | |
| "middle": [], | |
| "last": "Develder", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "2830--2836", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.18653/v1/D18-1307" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Giannis Bekoulis, Johannes Deleu, Thomas Demeester, and Chris Develder. 2018. Adversarial training for multi-context joint entity and relation extrac- tion. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Process- ing, pages 2830-2836, Brussels, Belgium. Associ- ation for Computational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF4": { | |
| "ref_id": "b4", | |
| "title": "Crosslingual distant supervision for extracting relations of different complexity", | |
| "authors": [ | |
| { | |
| "first": "Andre", | |
| "middle": [], | |
| "last": "Blessing", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Hinrich", | |
| "middle": [], | |
| "last": "Sch\u00fctze", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2012, | |
| "venue": "Proceedings of the 21st ACM international conference on Information and knowledge management", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Andre Blessing and Hinrich Sch\u00fctze. 2012. Crosslin- gual distant supervision for extracting relations of different complexity. In Proceedings of the 21st ACM international conference on Information and knowledge management.", | |
| "links": null | |
| }, | |
| "BIBREF5": { | |
| "ref_id": "b5", | |
| "title": "Enriching word vectors with subword information", | |
| "authors": [ | |
| { | |
| "first": "Piotr", | |
| "middle": [], | |
| "last": "Bojanowski", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Edouard", | |
| "middle": [], | |
| "last": "Grave", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Armand", | |
| "middle": [], | |
| "last": "Joulin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Tomas", | |
| "middle": [], | |
| "last": "Mikolov", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Transactions of the Association for Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the Associa- tion for Computational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF6": { | |
| "ref_id": "b6", | |
| "title": "Joint modeling for Chinese event extraction with rich linguistic features", | |
| "authors": [ | |
| { | |
| "first": "Chen", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ng", | |
| "middle": [], | |
| "last": "Vincent", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2012, | |
| "venue": "Proceedings of the 24th International Conference on Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Chen Chen and Ng Vincent. 2012. Joint modeling for Chinese event extraction with rich linguistic fea- tures. In Proceedings of the 24th International Con- ference on Computational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF7": { | |
| "ref_id": "b7", | |
| "title": "Event extraction via dynamic multipooling convolutional neural networks", | |
| "authors": [ | |
| { | |
| "first": "Yubo", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Liheng", | |
| "middle": [], | |
| "last": "Xu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Kang", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Daojian", | |
| "middle": [], | |
| "last": "Zeng", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, and Jun Zhao. 2015. Event extraction via dynamic multi- pooling convolutional neural networks. In Proceed- ings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Interna- tional Joint Conference on Natural Language Pro- cessing (Volume 1: Long Papers).", | |
| "links": null | |
| }, | |
| "BIBREF8": { | |
| "ref_id": "b8", | |
| "title": "Can one language bootstrap the other: a case study on event extraction", | |
| "authors": [ | |
| { | |
| "first": "Zheng", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Heng", | |
| "middle": [], | |
| "last": "Ji", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2009, | |
| "venue": "Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Zheng Chen and Heng Ji. 2009. Can one language bootstrap the other: a case study on event extraction. In Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing.", | |
| "links": null | |
| }, | |
| "BIBREF9": { | |
| "ref_id": "b9", | |
| "title": "A survey of multilingual event extraction from text", | |
| "authors": [ | |
| { | |
| "first": "Vera", | |
| "middle": [], | |
| "last": "Danilova", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mikhail", | |
| "middle": [], | |
| "last": "Alexandrov", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Xavier", | |
| "middle": [], | |
| "last": "Blanco", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Proceedings of the International Conference on Applications of Natural Language to Data Bases/Information Systems", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Vera Danilova, Mikhail Alexandrov, and Xavier Blanco. 2014. A survey of multilingual event extrac- tion from text. In Proceedings of the International Conference on Applications of Natural Language to Data Bases/Information Systems.", | |
| "links": null | |
| }, | |
| "BIBREF10": { | |
| "ref_id": "b10", | |
| "title": "The automatic content extraction (ACE) program-tasks, data, and evaluation", | |
| "authors": [ | |
| { | |
| "first": "Alexis", | |
| "middle": [], | |
| "last": "George R Doddington", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Mitchell", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "A", | |
| "middle": [], | |
| "last": "Mark", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Przybocki", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "A", | |
| "middle": [], | |
| "last": "Lance", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Stephanie", | |
| "middle": [ | |
| "M" | |
| ], | |
| "last": "Ramshaw", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ralph", | |
| "middle": [ | |
| "M" | |
| ], | |
| "last": "Strassel", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Weischedel", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2004, | |
| "venue": "Proceedings of the Fourth International Conference on Language Resources and Evaluation", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "George R Doddington, Alexis Mitchell, Mark A Przy- bocki, Lance A Ramshaw, Stephanie M Strassel, and Ralph M Weischedel. 2004. The automatic content extraction (ACE) program-tasks, data, and evalua- tion. In Proceedings of the Fourth International Conference on Language Resources and Evaluation.", | |
| "links": null | |
| }, | |
| "BIBREF11": { | |
| "ref_id": "b11", | |
| "title": "Improving vector space word representations using multilingual correlation", | |
| "authors": [ | |
| { | |
| "first": "Manaal", | |
| "middle": [], | |
| "last": "Faruqui", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Chris", | |
| "middle": [], | |
| "last": "Dyer", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Manaal Faruqui and Chris Dyer. 2014. Improving vec- tor space word representations using multilingual correlation. In Proceedings of the 14th Conference of the European Chapter of the Association for Com- putational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF12": { | |
| "ref_id": "b12", | |
| "title": "Multilingual open relation extraction using cross-lingual projection", | |
| "authors": [ | |
| { | |
| "first": "Manaal", | |
| "middle": [], | |
| "last": "Faruqui", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Shankar", | |
| "middle": [], | |
| "last": "Kumar", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Manaal Faruqui and Shankar Kumar. 2015. Multilin- gual open relation extraction using cross-lingual pro- jection. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies.", | |
| "links": null | |
| }, | |
| "BIBREF13": { | |
| "ref_id": "b13", | |
| "title": "A languageindependent neural network for event detection", | |
| "authors": [ | |
| { | |
| "first": "Xiaocheng", | |
| "middle": [], | |
| "last": "Feng", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Lifu", | |
| "middle": [], | |
| "last": "Huang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Duyu", | |
| "middle": [], | |
| "last": "Tang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Heng", | |
| "middle": [], | |
| "last": "Ji", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Bing", | |
| "middle": [], | |
| "last": "Qin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ting", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "2", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Xiaocheng Feng, Lifu Huang, Duyu Tang, Heng Ji, Bing Qin, and Ting Liu. 2016. A language- independent neural network for event detection. In Proceedings of the 54th Annual Meeting of the As- sociation for Computational Linguistics (Volume 2: Short Papers).", | |
| "links": null | |
| }, | |
| "BIBREF14": { | |
| "ref_id": "b14", | |
| "title": "Laying the groundwork for knowledge base population: Nine years of linguistic resources for TAC KBP", | |
| "authors": [ | |
| { | |
| "first": "Jeremy", | |
| "middle": [], | |
| "last": "Getman", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Joe", | |
| "middle": [], | |
| "last": "Ellis", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Stephanie", | |
| "middle": [], | |
| "last": "Strassel", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Zhiyi", | |
| "middle": [], | |
| "last": "Song", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jennifer", | |
| "middle": [], | |
| "last": "Tracey", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Jeremy Getman, Joe Ellis, Stephanie Strassel, Zhiyi Song, and Jennifer Tracey. 2018. Laying the ground- work for knowledge base population: Nine years of linguistic resources for TAC KBP. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018).", | |
| "links": null | |
| }, | |
| "BIBREF15": { | |
| "ref_id": "b15", | |
| "title": "Leveraging multilingual training for limited resource event extraction", | |
| "authors": [ | |
| { | |
| "first": "Andrew", | |
| "middle": [], | |
| "last": "Hsi", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yiming", | |
| "middle": [], | |
| "last": "Yang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jaime", | |
| "middle": [], | |
| "last": "Carbonell", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ruochen", | |
| "middle": [], | |
| "last": "Xu", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Andrew Hsi, Yiming Yang, Jaime Carbonell, and Ruochen Xu. 2016. Leveraging multilingual train- ing for limited resource event extraction. In Pro- ceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Techni- cal Papers.", | |
| "links": null | |
| }, | |
| "BIBREF16": { | |
| "ref_id": "b16", | |
| "title": "Crosslingual multi-level adversarial transfer to enhance low-resource name tagging", | |
| "authors": [ | |
| { | |
| "first": "Lifu", | |
| "middle": [], | |
| "last": "Huang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ji", | |
| "middle": [], | |
| "last": "Heng", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jonathan", | |
| "middle": [], | |
| "last": "", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2019, | |
| "venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Lifu Huang, Heng Ji, and Jonathan May. 2019. Cross- lingual multi-level adversarial transfer to enhance low-resource name tagging. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Hu- man Language Technologies, Volume 1 (Long and Short Papers).", | |
| "links": null | |
| }, | |
| "BIBREF17": { | |
| "ref_id": "b17", | |
| "title": "Refining event extraction through cross-document inference", | |
| "authors": [ | |
| { | |
| "first": "Heng", | |
| "middle": [], | |
| "last": "Ji", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ralph", | |
| "middle": [], | |
| "last": "Grishman", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2008, | |
| "venue": "Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Heng Ji and Ralph Grishman. 2008. Refining event ex- traction through cross-document inference. In Pro- ceedings of the 46th Annual Meeting of the Associ- ation for Computational Linguistics: Human Lan- guage Technologies.", | |
| "links": null | |
| }, | |
| "BIBREF18": { | |
| "ref_id": "b18", | |
| "title": "Loss in translation: Learning bilingual word mapping with a retrieval criterion", | |
| "authors": [ | |
| { | |
| "first": "Armand", | |
| "middle": [], | |
| "last": "Joulin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Piotr", | |
| "middle": [], | |
| "last": "Bojanowski", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Tomas", | |
| "middle": [], | |
| "last": "Mikolov", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Herv\u00e9", | |
| "middle": [], | |
| "last": "J\u00e9gou", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Edouard", | |
| "middle": [], | |
| "last": "Grave", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Armand Joulin, Piotr Bojanowski, Tomas Mikolov, Herv\u00e9 J\u00e9gou, and Edouard Grave. 2018. Loss in translation: Learning bilingual word mapping with a retrieval criterion. In Proceedings of the 2018 Con- ference on Empirical Methods in Natural Language Processing.", | |
| "links": null | |
| }, | |
| "BIBREF19": { | |
| "ref_id": "b19", | |
| "title": "A cross-lingual annotation projection approach for relation detection", | |
| "authors": [ | |
| { | |
| "first": "Seokhwan", | |
| "middle": [], | |
| "last": "Kim", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Minwoo", | |
| "middle": [], | |
| "last": "Jeong", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jonghoon", | |
| "middle": [], | |
| "last": "Lee", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Gary Geunbae", | |
| "middle": [], | |
| "last": "Lee", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2010, | |
| "venue": "Proceedings of the 23rd International Conference on Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Seokhwan Kim, Minwoo Jeong, Jonghoon Lee, and Gary Geunbae Lee. 2010. A cross-lingual annota- tion projection approach for relation detection. In Proceedings of the 23rd International Conference on Computational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF20": { | |
| "ref_id": "b20", | |
| "title": "Semisupervised classification with graph convolutional networks", | |
| "authors": [ | |
| { | |
| "first": "N", | |
| "middle": [], | |
| "last": "Thomas", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Max", | |
| "middle": [], | |
| "last": "Kipf", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Welling", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "International Conference on Learning Representations", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Thomas N. Kipf and Max Welling. 2017. Semi- supervised classification with graph convolutional networks. In International Conference on Learning Representations.", | |
| "links": null | |
| }, | |
| "BIBREF21": { | |
| "ref_id": "b21", | |
| "title": "Crosslingual transfer of semantic role labeling models", | |
| "authors": [ | |
| { | |
| "first": "Mikhail", | |
| "middle": [], | |
| "last": "Kozhevnikov", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ivan", | |
| "middle": [], | |
| "last": "Titov", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2013, | |
| "venue": "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Mikhail Kozhevnikov and Ivan Titov. 2013. Cross- lingual transfer of semantic role labeling models. In Proceedings of the 51st Annual Meeting of the Asso- ciation for Computational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF22": { | |
| "ref_id": "b22", | |
| "title": "Hubness and pollution: Delving into cross-space mapping for zero-shot learning", | |
| "authors": [ | |
| { | |
| "first": "Angeliki", | |
| "middle": [], | |
| "last": "Lazaridou", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Georgiana", | |
| "middle": [], | |
| "last": "Dinu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Marco", | |
| "middle": [], | |
| "last": "Baroni", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Angeliki Lazaridou, Georgiana Dinu, and Marco Ba- roni. 2015. Hubness and pollution: Delving into cross-space mapping for zero-shot learning. In Pro- ceedings of the 53rd Annual Meeting of the Associ- ation for Computational Linguistics and the 7th In- ternational Joint Conference on Natural Language Processing (Volume 1: Long Papers).", | |
| "links": null | |
| }, | |
| "BIBREF23": { | |
| "ref_id": "b23", | |
| "title": "End-to-end neural coreference resolution", | |
| "authors": [ | |
| { | |
| "first": "Kenton", | |
| "middle": [], | |
| "last": "Lee", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Luheng", | |
| "middle": [], | |
| "last": "He", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mike", | |
| "middle": [], | |
| "last": "Lewis", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Luke", | |
| "middle": [], | |
| "last": "Zettlemoyer", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Kenton Lee, Luheng He, Mike Lewis, and Luke Zettle- moyer. 2017. End-to-end neural coreference resolu- tion. In Proceedings of the 2017 Conference on Em- pirical Methods in Natural Language Processing.", | |
| "links": null | |
| }, | |
| "BIBREF24": { | |
| "ref_id": "b24", | |
| "title": "Employing compositional semantics and discourse consistency in chinese event extraction", | |
| "authors": [ | |
| { | |
| "first": "Peifeng", | |
| "middle": [], | |
| "last": "Li", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Guodong", | |
| "middle": [], | |
| "last": "Zhou", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Qiaoming", | |
| "middle": [], | |
| "last": "Zhu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Libin", | |
| "middle": [], | |
| "last": "Hou", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2012, | |
| "venue": "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Peifeng Li, Guodong Zhou, Qiaoming Zhu, and Libin Hou. 2012. Employing compositional semantics and discourse consistency in chinese event extraction. In Proceedings of the 2012 Joint Conference on Empir- ical Methods in Natural Language Processing and Computational Natural Language Learning.", | |
| "links": null | |
| }, | |
| "BIBREF25": { | |
| "ref_id": "b25", | |
| "title": "Incremental joint extraction of entity mentions and relations", | |
| "authors": [ | |
| { | |
| "first": "Qi", | |
| "middle": [], | |
| "last": "Li", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Heng", | |
| "middle": [], | |
| "last": "Ji", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Qi Li and Heng Ji. 2014. Incremental joint extraction of entity mentions and relations. In Proceedings of the 52nd Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers).", | |
| "links": null | |
| }, | |
| "BIBREF26": { | |
| "ref_id": "b26", | |
| "title": "Joint event extraction via structured prediction with global features", | |
| "authors": [ | |
| { | |
| "first": "Qi", | |
| "middle": [], | |
| "last": "Li", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ji", | |
| "middle": [], | |
| "last": "Heng", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Liang", | |
| "middle": [], | |
| "last": "Huang", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2013, | |
| "venue": "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Qi Li, Heng Ji, and Liang Huang. 2013. Joint event extraction via structured prediction with global fea- tures. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers).", | |
| "links": null | |
| }, | |
| "BIBREF27": { | |
| "ref_id": "b27", | |
| "title": "Constructing information networks using one single model", | |
| "authors": [ | |
| { | |
| "first": "Qi", | |
| "middle": [], | |
| "last": "Li", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Heng", | |
| "middle": [], | |
| "last": "Ji", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yu", | |
| "middle": [], | |
| "last": "Hong", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Sujian", | |
| "middle": [], | |
| "last": "Li", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Qi Li, Heng Ji, HONG Yu, and Sujian Li. 2014. Constructing information networks using one single model. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Process- ing.", | |
| "links": null | |
| }, | |
| "BIBREF28": { | |
| "ref_id": "b28", | |
| "title": "Acquiring topic features to improve event extraction: in preselected and balanced collections", | |
| "authors": [ | |
| { | |
| "first": "Shasha", | |
| "middle": [], | |
| "last": "Liao", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ralph", | |
| "middle": [], | |
| "last": "Grishman", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2011, | |
| "venue": "Proceedings of the International Conference Recent Advances in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Shasha Liao and Ralph Grishman. 2011. Acquiring topic features to improve event extraction: in pre- selected and balanced collections. In Proceedings of the International Conference Recent Advances in Natural Language Processing 2011.", | |
| "links": null | |
| }, | |
| "BIBREF29": { | |
| "ref_id": "b29", | |
| "title": "Neural relation extraction with multi-lingual attention", | |
| "authors": [ | |
| { | |
| "first": "Yankai", | |
| "middle": [], | |
| "last": "Lin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Zhiyuan", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Maosong", | |
| "middle": [], | |
| "last": "Sun", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Yankai Lin, Zhiyuan Liu, and Maosong Sun. 2017. Neural relation extraction with multi-lingual atten- tion. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers).", | |
| "links": null | |
| }, | |
| "BIBREF30": { | |
| "ref_id": "b30", | |
| "title": "A multi-lingual multi-task architecture for low-resource sequence labeling", | |
| "authors": [ | |
| { | |
| "first": "Ying", | |
| "middle": [], | |
| "last": "Lin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Shengqi", | |
| "middle": [], | |
| "last": "Yang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Veselin", | |
| "middle": [], | |
| "last": "Stoyanov", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Heng", | |
| "middle": [], | |
| "last": "Ji", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proc. The 56th Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ying Lin, Shengqi Yang, Veselin Stoyanov, and Heng Ji. 2018. A multi-lingual multi-task architecture for low-resource sequence labeling. In Proc. The 56th Annual Meeting of the Association for Compu- tational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF31": { | |
| "ref_id": "b31", | |
| "title": "Event detection via gated multilingual attention mechanism", | |
| "authors": [ | |
| { | |
| "first": "Jian", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yubo", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Kang", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Jian Liu, Yubo Chen, Kang Liu, and Jun Zhao. 2018a. Event detection via gated multilingual attention mechanism. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.", | |
| "links": null | |
| }, | |
| "BIBREF32": { | |
| "ref_id": "b32", | |
| "title": "Linguistic knowledge and transferability of contextual representations", | |
| "authors": [ | |
| { | |
| "first": "Nelson", | |
| "middle": [ | |
| "F" | |
| ], | |
| "last": "Liu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Matt", | |
| "middle": [], | |
| "last": "Gardner", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yonatan", | |
| "middle": [], | |
| "last": "Belinkov", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Matthew", | |
| "middle": [ | |
| "E" | |
| ], | |
| "last": "Peters", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Noah", | |
| "middle": [ | |
| "A" | |
| ], | |
| "last": "Smith", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2019, | |
| "venue": "Proceedings of the Conference of the North American Chapter", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith. 2019. Lin- guistic knowledge and transferability of contextual representations. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF33": { | |
| "ref_id": "b33", | |
| "title": "Jointly multiple events extraction via attention-based graph information aggregation", | |
| "authors": [ | |
| { | |
| "first": "Xiao", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Zhunchen", | |
| "middle": [], | |
| "last": "Luo", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Heyan", | |
| "middle": [], | |
| "last": "Huang", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Xiao Liu, Zhunchen Luo, and Heyan Huang. 2018b. Jointly multiple events extraction via attention-based graph information aggregation. In Proceedings of the 2018 Conference on Empirical Methods in Nat- ural Language Processing.", | |
| "links": null | |
| }, | |
| "BIBREF34": { | |
| "ref_id": "b34", | |
| "title": "Deep multilingual correlation for improved word embeddings", | |
| "authors": [ | |
| { | |
| "first": "Ang", | |
| "middle": [], | |
| "last": "Lu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Weiran", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mohit", | |
| "middle": [], | |
| "last": "Bansal", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Kevin", | |
| "middle": [], | |
| "last": "Gimpel", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Karen", | |
| "middle": [], | |
| "last": "Livescu", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ang Lu, Weiran Wang, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2015. Deep multilingual corre- lation for improved word embeddings. In Proceed- ings of the 2015 Conference of the North American Chapter of the Association for Computational Lin- guistics: Human Language Technologies.", | |
| "links": null | |
| }, | |
| "BIBREF35": { | |
| "ref_id": "b35", | |
| "title": "Similar but not the same: Word sense disambiguation improves event detection via neural representation matching", | |
| "authors": [ | |
| { | |
| "first": "Weiyi", | |
| "middle": [], | |
| "last": "Lu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Thien", | |
| "middle": [], | |
| "last": "Huu Nguyen", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Weiyi Lu and Thien Huu Nguyen. 2018. Similar but not the same: Word sense disambiguation improves event detection via neural representation matching. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.", | |
| "links": null | |
| }, | |
| "BIBREF36": { | |
| "ref_id": "b36", | |
| "title": "The Stanford CoreNLP natural language processing toolkit", | |
| "authors": [ | |
| { | |
| "first": "Christopher", | |
| "middle": [], | |
| "last": "Manning", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mihai", | |
| "middle": [], | |
| "last": "Surdeanu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "John", | |
| "middle": [], | |
| "last": "Bauer", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jenny", | |
| "middle": [], | |
| "last": "Finkel", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Steven", | |
| "middle": [], | |
| "last": "Bethard", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "David", | |
| "middle": [], | |
| "last": "Mcclosky", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Proceedings of 52nd annual meeting of the Association for Computational Linguistics: system demonstrations", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014. The Stanford CoreNLP natural language pro- cessing toolkit. In Proceedings of 52nd annual meet- ing of the Association for Computational Linguis- tics: system demonstrations.", | |
| "links": null | |
| }, | |
| "BIBREF37": { | |
| "ref_id": "b37", | |
| "title": "Encoding sentences with graph convolutional networks for semantic role labeling", | |
| "authors": [ | |
| { | |
| "first": "Diego", | |
| "middle": [], | |
| "last": "Marcheggiani", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ivan", | |
| "middle": [], | |
| "last": "Titov", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Diego Marcheggiani and Ivan Titov. 2017. Encoding sentences with graph convolutional networks for se- mantic role labeling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Lan- guage Processing.", | |
| "links": null | |
| }, | |
| "BIBREF38": { | |
| "ref_id": "b38", | |
| "title": "Cheap translation for cross-lingual named entity recognition", | |
| "authors": [ | |
| { | |
| "first": "Stephen", | |
| "middle": [], | |
| "last": "Mayhew", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Chen-Tse", | |
| "middle": [], | |
| "last": "Tsai", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Dan", | |
| "middle": [], | |
| "last": "Roth", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Stephen Mayhew, Chen-Tse Tsai, and Dan Roth. 2017. Cheap translation for cross-lingual named entity recognition. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Process- ing.", | |
| "links": null | |
| }, | |
| "BIBREF39": { | |
| "ref_id": "b39", | |
| "title": "Exploiting similarities among languages for machine translation", | |
| "authors": [ | |
| { | |
| "first": "Tomas", | |
| "middle": [], | |
| "last": "Mikolov", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "V", | |
| "middle": [], | |
| "last": "Quoc", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ilya", | |
| "middle": [], | |
| "last": "Le", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Sutskever", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2013, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": { | |
| "arXiv": [ | |
| "arXiv:1309.4168" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Tomas Mikolov, Quoc V Le, and Ilya Sutskever. 2013. Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168.", | |
| "links": null | |
| }, | |
| "BIBREF40": { | |
| "ref_id": "b40", | |
| "title": "Distant supervision for relation extraction with an incomplete knowledge base", | |
| "authors": [ | |
| { | |
| "first": "Bonan", | |
| "middle": [], | |
| "last": "Min", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ralph", | |
| "middle": [], | |
| "last": "Grishman", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Li", | |
| "middle": [], | |
| "last": "Wan", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Chang", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "David", | |
| "middle": [], | |
| "last": "Gondek", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2013, | |
| "venue": "Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Bonan Min, Ralph Grishman, Li Wan, Chang Wang, and David Gondek. 2013. Distant supervision for relation extraction with an incomplete knowledge base. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Com- putational Linguistics: Human Language Technolo- gies.", | |
| "links": null | |
| }, | |
| "BIBREF41": { | |
| "ref_id": "b41", | |
| "title": "Distant supervision for relation extraction without labeled data", | |
| "authors": [ | |
| { | |
| "first": "Mike", | |
| "middle": [], | |
| "last": "Mintz", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Steven", | |
| "middle": [], | |
| "last": "Bills", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Rion", | |
| "middle": [], | |
| "last": "Snow", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Dan", | |
| "middle": [], | |
| "last": "Jurafsky", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2009, | |
| "venue": "Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Mike Mintz, Steven Bills, Rion Snow, and Dan Juraf- sky. 2009. Distant supervision for relation extrac- tion without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natu- ral Language Processing of the AFNLP.", | |
| "links": null | |
| }, | |
| "BIBREF42": { | |
| "ref_id": "b42", | |
| "title": "End-to-end relation extraction using LSTMs on sequences and tree structures", | |
| "authors": [ | |
| { | |
| "first": "Makoto", | |
| "middle": [], | |
| "last": "Miwa", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mohit", | |
| "middle": [], | |
| "last": "Bansal", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Makoto Miwa and Mohit Bansal. 2016. End-to-end re- lation extraction using LSTMs on sequences and tree structures. In Proceedings of the 54th Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers).", | |
| "links": null | |
| }, | |
| "BIBREF43": { | |
| "ref_id": "b43", | |
| "title": "Polyglot contextual representations improve crosslingual transfer", | |
| "authors": [ | |
| { | |
| "first": "Phoebe", | |
| "middle": [], | |
| "last": "Mulcaire", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jungo", | |
| "middle": [], | |
| "last": "Kasai", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Noah", | |
| "middle": [ | |
| "A" | |
| ], | |
| "last": "Smith", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2019, | |
| "venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Phoebe Mulcaire, Jungo Kasai, and Noah A. Smith. 2019. Polyglot contextual representations improve crosslingual transfer. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers).", | |
| "links": null | |
| }, | |
| "BIBREF44": { | |
| "ref_id": "b44", | |
| "title": "Polyglot semantic role labeling", | |
| "authors": [ | |
| { | |
| "first": "Phoebe", | |
| "middle": [], | |
| "last": "Mulcaire", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Swabha", | |
| "middle": [], | |
| "last": "Swayamdipta", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Noah", | |
| "middle": [ | |
| "A" | |
| ], | |
| "last": "Smith", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "2", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Phoebe Mulcaire, Swabha Swayamdipta, and Noah A. Smith. 2018. Polyglot semantic role labeling. In Proceedings of the 56th Annual Meeting of the As- sociation for Computational Linguistics (Volume 2: Short Papers).", | |
| "links": null | |
| }, | |
| "BIBREF45": { | |
| "ref_id": "b45", | |
| "title": "Joint event extraction via recurrent neural networks", | |
| "authors": [ | |
| { | |
| "first": "Kyunghyun", | |
| "middle": [], | |
| "last": "Thien Huu Nguyen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ralph", | |
| "middle": [], | |
| "last": "Cho", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Grishman", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Thien Huu Nguyen, Kyunghyun Cho, and Ralph Grish- man. 2016. Joint event extraction via recurrent neu- ral networks. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies.", | |
| "links": null | |
| }, | |
| "BIBREF46": { | |
| "ref_id": "b46", | |
| "title": "Event detection and domain adaptation with convolutional neural networks", | |
| "authors": [ | |
| { | |
| "first": "Huu", | |
| "middle": [], | |
| "last": "Thien", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ralph", | |
| "middle": [], | |
| "last": "Nguyen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Grishman", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing", | |
| "volume": "2", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Thien Huu Nguyen and Ralph Grishman. 2015a. Event detection and domain adaptation with convolutional neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Lin- guistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers).", | |
| "links": null | |
| }, | |
| "BIBREF47": { | |
| "ref_id": "b47", | |
| "title": "Relation extraction: Perspective from convolutional neural networks", | |
| "authors": [ | |
| { | |
| "first": "Huu", | |
| "middle": [], | |
| "last": "Thien", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ralph", | |
| "middle": [], | |
| "last": "Nguyen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Grishman", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Proceedings of North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Thien Huu Nguyen and Ralph Grishman. 2015b. Rela- tion extraction: Perspective from convolutional neu- ral networks. In Proceedings of North American Chapter of the Association for Computational Lin- guistics: Human Language Technologies.", | |
| "links": null | |
| }, | |
| "BIBREF48": { | |
| "ref_id": "b48", | |
| "title": "End-to-end relation extraction using neural networks and Markov logic networks", | |
| "authors": [ | |
| { | |
| "first": "Sachin", | |
| "middle": [], | |
| "last": "Pawar", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Pushpak", | |
| "middle": [], | |
| "last": "Bhattacharyya", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Girish", | |
| "middle": [ | |
| "K" | |
| ], | |
| "last": "Palshikar", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Sachin Pawar, Pushpak Bhattacharyya, and Girish K. Palshikar. 2017. End-to-end relation extraction us- ing neural networks and Markov logic networks. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Lin- guistics.", | |
| "links": null | |
| }, | |
| "BIBREF49": { | |
| "ref_id": "b49", | |
| "title": "Scaling up automatic cross-lingual semantic role annotation", | |
| "authors": [ | |
| { | |
| "first": "Lonneke", | |
| "middle": [], | |
| "last": "Van Der Plas", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Paola", | |
| "middle": [], | |
| "last": "Merlo", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "James", | |
| "middle": [], | |
| "last": "Henderson", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2011, | |
| "venue": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Lonneke Van der Plas, Paola Merlo, and James Hen- derson. 2011. Scaling up automatic cross-lingual se- mantic role annotation. In Proceedings of the 49th Annual Meeting of the Association for Computa- tional Linguistics: Human Language Technologies.", | |
| "links": null | |
| }, | |
| "BIBREF50": { | |
| "ref_id": "b50", | |
| "title": "Crosslingual SRL based upon universal dependencies", | |
| "authors": [ | |
| { | |
| "first": "Ondrej", | |
| "middle": [], | |
| "last": "Praz\u00e1k", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Miloslav", | |
| "middle": [], | |
| "last": "Konop\u00edk", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Proceedings of the International Conference Recent Advances in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.26615/978-954-452-049-6_077" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ondrej Praz\u00e1k and Miloslav Konop\u00edk. 2017. Cross- lingual SRL based upon universal dependencies. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017.", | |
| "links": null | |
| }, | |
| "BIBREF51": { | |
| "ref_id": "b51", | |
| "title": "Bilingual active learning for relation classification via pseudo parallel corpora", | |
| "authors": [ | |
| { | |
| "first": "Longhua", | |
| "middle": [], | |
| "last": "Qian", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Haotian", | |
| "middle": [], | |
| "last": "Hui", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ya'nan", | |
| "middle": [], | |
| "last": "Hu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Guodong", | |
| "middle": [], | |
| "last": "Zhou", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Qiaoming", | |
| "middle": [], | |
| "last": "Zhu", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Longhua Qian, Haotian Hui, Ya'nan Hu, Guodong Zhou, and Qiaoming Zhu. 2014. Bilingual active learning for relation classification via pseudo par- allel corpora. In Proceedings of the 52nd Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers).", | |
| "links": null | |
| }, | |
| "BIBREF52": { | |
| "ref_id": "b52", | |
| "title": "Dsgan: Generative adversarial training for distant supervision relation extraction", | |
| "authors": [ | |
| { | |
| "first": "Pengda", | |
| "middle": [], | |
| "last": "Qin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Weiran", | |
| "middle": [], | |
| "last": "Xu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "William", | |
| "middle": [ | |
| "Yang" | |
| ], | |
| "last": "Wang", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Pengda Qin, Weiran Xu, and William Yang Wang. 2018. Dsgan: Generative adversarial training for distant supervision relation extraction. In Proceed- ings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa- pers).", | |
| "links": null | |
| }, | |
| "BIBREF53": { | |
| "ref_id": "b53", | |
| "title": "Distant supervision for relation extraction beyond the sentence boundary", | |
| "authors": [ | |
| { | |
| "first": "Chris", | |
| "middle": [], | |
| "last": "Quirk", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Hoifung", | |
| "middle": [], | |
| "last": "Poon", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Chris Quirk and Hoifung Poon. 2017. Distant super- vision for relation extraction beyond the sentence boundary. In Proceedings of the 15th Conference of the European Chapter of the Association for Compu- tational Linguistics: Volume 1, Long Papers.", | |
| "links": null | |
| }, | |
| "BIBREF54": { | |
| "ref_id": "b54", | |
| "title": "Ultradense word embeddings by orthogonal transformation", | |
| "authors": [ | |
| { | |
| "first": "Sascha", | |
| "middle": [], | |
| "last": "Rothe", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Sebastian", | |
| "middle": [], | |
| "last": "Ebert", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Hinrich", | |
| "middle": [], | |
| "last": "Sch\u00fctze", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Sascha Rothe, Sebastian Ebert, and Hinrich Sch\u00fctze. 2016. Ultradense word embeddings by orthogonal transformation. In Proceedings of the 2016 Confer- ence of the North American Chapter of the Associ- ation for Computational Linguistics: Human Lan- guage Technologies.", | |
| "links": null | |
| }, | |
| "BIBREF55": { | |
| "ref_id": "b55", | |
| "title": "A simple neural network module for relational reasoning", | |
| "authors": [ | |
| { | |
| "first": "Adam", | |
| "middle": [], | |
| "last": "Santoro", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "David", | |
| "middle": [], | |
| "last": "Raposo", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "G", | |
| "middle": [], | |
| "last": "David", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mateusz", | |
| "middle": [], | |
| "last": "Barrett", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Razvan", | |
| "middle": [], | |
| "last": "Malinowski", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Peter", | |
| "middle": [], | |
| "last": "Pascanu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Timothy", | |
| "middle": [], | |
| "last": "Battaglia", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Lillicrap", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Proceedings of Advances in Neural Information Processing Systems", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Adam Santoro, David Raposo, David G Barrett, Ma- teusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap. 2017. A simple neural net- work module for relational reasoning. In Proceed- ings of Advances in Neural Information Processing Systems.", | |
| "links": null | |
| }, | |
| "BIBREF56": { | |
| "ref_id": "b56", | |
| "title": "Offline bilingual word vectors, orthogonal transformations and the inverted softmax", | |
| "authors": [ | |
| { | |
| "first": "L", | |
| "middle": [], | |
| "last": "Samuel", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Smith", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "H", | |
| "middle": [ | |
| "P" | |
| ], | |
| "last": "David", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Steven", | |
| "middle": [], | |
| "last": "Turban", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Nils", | |
| "middle": [ | |
| "Y" | |
| ], | |
| "last": "Hamblin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Hammerla", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": { | |
| "arXiv": [ | |
| "arXiv:1702.03859" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Samuel L Smith, David HP Turban, Steven Hamblin, and Nils Y Hammerla. 2017. Offline bilingual word vectors, orthogonal transformations and the inverted softmax. arXiv preprint arXiv:1702.03859.", | |
| "links": null | |
| }, | |
| "BIBREF57": { | |
| "ref_id": "b57", | |
| "title": "Tokenizing, POS tagging, lemmatizing and parsing UD 2.0 with UDPipe", | |
| "authors": [ | |
| { | |
| "first": "Milan", | |
| "middle": [], | |
| "last": "Straka", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jana", | |
| "middle": [], | |
| "last": "Strakov\u00e1", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Milan Straka and Jana Strakov\u00e1. 2017. Tokenizing, POS tagging, lemmatizing and parsing UD 2.0 with UDPipe. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Uni- versal Dependencies.", | |
| "links": null | |
| }, | |
| "BIBREF58": { | |
| "ref_id": "b58", | |
| "title": "Multi-instance multi-label learning for relation extraction", | |
| "authors": [ | |
| { | |
| "first": "Mihai", | |
| "middle": [], | |
| "last": "Surdeanu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Julie", | |
| "middle": [], | |
| "last": "Tibshirani", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ramesh", | |
| "middle": [], | |
| "last": "Nallapati", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Christopher", | |
| "middle": [ | |
| "D" | |
| ], | |
| "last": "Manning", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2012, | |
| "venue": "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati, and Christopher D. Manning. 2012. Multi-instance multi-label learning for relation extraction. In Pro- ceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Com- putational Natural Language Learning.", | |
| "links": null | |
| }, | |
| "BIBREF59": { | |
| "ref_id": "b59", | |
| "title": "Improved semantic representations from tree-structured long short-term memory networks", | |
| "authors": [ | |
| { | |
| "first": "Kai Sheng", | |
| "middle": [], | |
| "last": "Tai", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Richard", | |
| "middle": [], | |
| "last": "Socher", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Christopher", | |
| "middle": [ | |
| "D" | |
| ], | |
| "last": "Manning", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015. Improved semantic representations from tree-structured long short-term memory net- works. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers).", | |
| "links": null | |
| }, | |
| "BIBREF60": { | |
| "ref_id": "b60", | |
| "title": "Ace 2005 multilingual training corpus. Linguistic Data Consortium", | |
| "authors": [ | |
| { | |
| "first": "Christopher", | |
| "middle": [], | |
| "last": "Walker", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Stephanie", | |
| "middle": [], | |
| "last": "Strassel", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Julie", | |
| "middle": [], | |
| "last": "Medero", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Kazuaki", | |
| "middle": [], | |
| "last": "Maeda", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2006, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Christopher Walker, Stephanie Strassel, Julie Medero, and Kazuaki Maeda. 2006. Ace 2005 multilin- gual training corpus. Linguistic Data Consortium, Philadelphia, 57.", | |
| "links": null | |
| }, | |
| "BIBREF61": { | |
| "ref_id": "b61", | |
| "title": "Label-free distant supervision for relation extraction via knowledge graph embedding", | |
| "authors": [ | |
| { | |
| "first": "Guanying", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Wen", | |
| "middle": [], | |
| "last": "Zhang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ruoxu", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yalin", | |
| "middle": [], | |
| "last": "Zhou", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Xi", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Wei", | |
| "middle": [], | |
| "last": "Zhang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Hai", | |
| "middle": [], | |
| "last": "Zhu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Huajun", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Guanying Wang, Wen Zhang, Ruoxu Wang, Yalin Zhou, Xi Chen, Wei Zhang, Hai Zhu, and Huajun Chen. 2018a. Label-free distant supervision for rela- tion extraction via knowledge graph embedding. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.", | |
| "links": null | |
| }, | |
| "BIBREF62": { | |
| "ref_id": "b62", | |
| "title": "Adversarial multi-lingual neural relation extraction", | |
| "authors": [ | |
| { | |
| "first": "Xiaozhi", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Xu", | |
| "middle": [], | |
| "last": "Han", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yankai", | |
| "middle": [], | |
| "last": "Lin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Zhiyuan", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Maosong", | |
| "middle": [], | |
| "last": "Sun", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 27th International Conference on Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Xiaozhi Wang, Xu Han, Yankai Lin, Zhiyuan Liu, and Maosong Sun. 2018b. Adversarial multi-lingual neural relation extraction. In Proceedings of the 27th International Conference on Computational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF63": { | |
| "ref_id": "b63", | |
| "title": "Normalized word embedding and orthogonal transform for bilingual word translation", | |
| "authors": [ | |
| { | |
| "first": "Chao", | |
| "middle": [], | |
| "last": "Xing", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Dong", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Chao", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yiye", | |
| "middle": [], | |
| "last": "Lin", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Chao Xing, Dong Wang, Chao Liu, and Yiye Lin. 2015. Normalized word embedding and orthogonal trans- form for bilingual word translation. In Proceedings of the 2015 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies.", | |
| "links": null | |
| }, | |
| "BIBREF64": { | |
| "ref_id": "b64", | |
| "title": "Joint extraction of events and entities within a document context", | |
| "authors": [ | |
| { | |
| "first": "Bishan", | |
| "middle": [], | |
| "last": "Yang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Tom", | |
| "middle": [ | |
| "M" | |
| ], | |
| "last": "Mitchell", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Bishan Yang and Tom M. Mitchell. 2016. Joint extrac- tion of events and entities within a document context. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies.", | |
| "links": null | |
| }, | |
| "BIBREF65": { | |
| "ref_id": "b65", | |
| "title": "Graph convolutional networks for text classification", | |
| "authors": [ | |
| { | |
| "first": "Liang", | |
| "middle": [], | |
| "last": "Yao", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Chengsheng", | |
| "middle": [], | |
| "last": "Mao", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yuan", | |
| "middle": [], | |
| "last": "Luo", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2019, | |
| "venue": "Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. Graph convolutional networks for text classification. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence.", | |
| "links": null | |
| }, | |
| "BIBREF66": { | |
| "ref_id": "b66", | |
| "title": "Distant supervision for relation extraction via piecewise convolutional neural networks", | |
| "authors": [ | |
| { | |
| "first": "Daojian", | |
| "middle": [], | |
| "last": "Zeng", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Kang", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao. 2015. Distant supervision for relation extraction via piecewise convolutional neural networks. In Pro- ceedings of the 2015 Conference on Empirical Meth- ods in Natural Language Processing.", | |
| "links": null | |
| }, | |
| "BIBREF67": { | |
| "ref_id": "b67", | |
| "title": "Relation classification via convolutional deep neural network", | |
| "authors": [ | |
| { | |
| "first": "Daojian", | |
| "middle": [], | |
| "last": "Zeng", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Kang", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Siwei", | |
| "middle": [], | |
| "last": "Lai", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Guangyou", | |
| "middle": [], | |
| "last": "Zhou", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jun", | |
| "middle": [], | |
| "last": "Zhao", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Proceedings of the 25th International Conference on Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. 2014. Relation classification via con- volutional deep neural network. In Proceedings of the 25th International Conference on Computational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF68": { | |
| "ref_id": "b68", | |
| "title": "Using entity relation to improve event detection via attention mechanism", | |
| "authors": [ | |
| { | |
| "first": "Jingli", | |
| "middle": [], | |
| "last": "Zhang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Wenxuan", | |
| "middle": [], | |
| "last": "Zhou", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yu", | |
| "middle": [], | |
| "last": "Hong", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jianmin", | |
| "middle": [], | |
| "last": "Yao", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Min", | |
| "middle": [], | |
| "last": "Zhang", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Jingli Zhang, Wenxuan Zhou, Yu Hong, Jianmin Yao, and Min Zhang. 2018a. Using entity relation to im- prove event detection via attention mechanism. In Proceedings of NLPCC 2018.", | |
| "links": null | |
| }, | |
| "BIBREF69": { | |
| "ref_id": "b69", | |
| "title": "Joint entity and event extraction with generative adversarial imitation learning", | |
| "authors": [ | |
| { | |
| "first": "Tongtao", | |
| "middle": [], | |
| "last": "Zhang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ji", | |
| "middle": [], | |
| "last": "Heng", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Avirup", | |
| "middle": [], | |
| "last": "Sil", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2019, | |
| "venue": "Data Intelligence", | |
| "volume": "1", | |
| "issue": "2", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Tongtao Zhang, Heng Ji, and Avirup Sil. 2019. Joint entity and event extraction with generative adversar- ial imitation learning. Data Intelligence Vol 1 (2).", | |
| "links": null | |
| }, | |
| "BIBREF70": { | |
| "ref_id": "b70", | |
| "title": "Ten pairs to tagmultilingual pos tagging via coarse mapping between embeddings", | |
| "authors": [ | |
| { | |
| "first": "Yuan", | |
| "middle": [], | |
| "last": "Zhang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "David", | |
| "middle": [], | |
| "last": "Gaddy", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Regina", | |
| "middle": [], | |
| "last": "Barzilay", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Tommi", | |
| "middle": [ | |
| "S" | |
| ], | |
| "last": "Jaakkola", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Yuan Zhang, David Gaddy, Regina Barzilay, and Tommi S Jaakkola. 2016. Ten pairs to tag- multilingual pos tagging via coarse mapping be- tween embeddings. In Proceedings of the 2016 Con- ference of the North American Chapter of the Asso- ciation for Computational Linguistics: Human Lan- guage Technologies.", | |
| "links": null | |
| }, | |
| "BIBREF71": { | |
| "ref_id": "b71", | |
| "title": "Graph convolution over pruned dependency trees improves relation extraction", | |
| "authors": [ | |
| { | |
| "first": "Yuhao", | |
| "middle": [], | |
| "last": "Zhang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Peng", | |
| "middle": [], | |
| "last": "Qi", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Christopher", | |
| "middle": [ | |
| "D" | |
| ], | |
| "last": "Manning", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Yuhao Zhang, Peng Qi, and Christopher D. Manning. 2018b. Graph convolution over pruned dependency trees improves relation extraction. In Proceedings of the 2018 Conference on Empirical Methods in Nat- ural Language Processing.", | |
| "links": null | |
| }, | |
| "BIBREF72": { | |
| "ref_id": "b72", | |
| "title": "Adversarial feature adaptation for cross-lingual relation classification", | |
| "authors": [ | |
| { | |
| "first": "Bowei", | |
| "middle": [], | |
| "last": "Zou", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Zengzhuang", | |
| "middle": [], | |
| "last": "Xu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yu", | |
| "middle": [], | |
| "last": "Hong", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Guodong", | |
| "middle": [], | |
| "last": "Zhou", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the 27th International Conference on Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Bowei Zou, Zengzhuang Xu, Yu Hong, and Guodong Zhou. 2018. Adversarial feature adaptation for cross-lingual relation classification. In Proceedings of the 27th International Conference on Computa- tional Linguistics.", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": { | |
| "FIGREF0": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "Information Network example constructed by Event (blue) and Relation (green dashed) Extraction.", | |
| "num": null | |
| }, | |
| "FIGREF1": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "\u0437\u0430\u043c\u0435\u0447\u0435\u043d\u044b \u0432 \u0443\u043f\u0430\u043a\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u043e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u044f\u0445 \u0441\u043a\u043e\u0440\u043e\u0439 \u043f\u043e\u043c\u043e\u0449\u0438 (teams of doctors were seen in packed emergency rooms)", | |
| "num": null | |
| }, | |
| "FIGREF2": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "Multilingual common semantic space and cross-lingual structure transfer.", | |
| "num": null | |
| }, | |
| "FIGREF3": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "For example, \"\u603b\u7edf(NOUN, president) \u8428\u59c6(PROPN, Samuel) \u2022(PUNCT) \u52aa\u4e54\u9a6c(PROPN, Nujoma)\" is mistakenly tagged as \"\u603b(NOUN) \u7edf\u8428\u59c6(PROPN) \u2022(PUNCT) \u52aa\u4e54(NOUN) \u9a6c(NOUN)\".", | |
| "num": null | |
| }, | |
| "FIGREF4": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "Relation Extraction: Comparison between supervised monolingual GCN model and cross-lingual transfer learning. Event Argument Role Labeling: Comparison between supervised monolingual GCN model and crosslingual transfer learning.", | |
| "num": null | |
| }, | |
| "TABREF1": { | |
| "num": null, | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table/>", | |
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| "num": null, | |
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| "html": null, | |
| "content": "<table><tr><td colspan=\"2\">shows the hy-</td></tr><tr><td>perparameters of our model.</td><td/></tr><tr><td>Hyperparameter</td><td>Value</td></tr><tr><td>word embedding size</td><td>300</td></tr><tr><td>POS embedding size</td><td>30</td></tr><tr><td>entity embedding size</td><td>30</td></tr><tr><td>dependency relation embedding size</td><td>30</td></tr><tr><td>hidden dimension size</td><td>200</td></tr><tr><td>dropout</td><td>0.5</td></tr><tr><td>number of layers</td><td>2</td></tr><tr><td>pooling function</td><td>max pooling</td></tr><tr><td>mlp layers</td><td>2</td></tr><tr><td>learning rate</td><td>0.1</td></tr><tr><td>learning rate decay</td><td>0.9</td></tr><tr><td>batch size</td><td>50</td></tr><tr><td>optimization</td><td>SGD</td></tr></table>", | |
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| }, | |
| "TABREF3": { | |
| "num": null, | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table/>", | |
| "text": "GCN hyperparameters." | |
| }, | |
| "TABREF5": { | |
| "num": null, | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td colspan=\"5\">: Relation Extraction: overall performance (F-</td></tr><tr><td colspan=\"4\">score %) using perfect entity mentions.</td></tr><tr><td>Train</td><td colspan=\"4\">Test English Chinese Arabic</td></tr><tr><td>English</td><td/><td>63.9</td><td>59.0</td><td>61.8</td></tr><tr><td>Chinese</td><td/><td>51.6</td><td>59.3</td><td>60.6</td></tr><tr><td>Arabic</td><td/><td>43.1</td><td>50.1</td><td>64.0</td></tr><tr><td colspan=\"2\">English + Chinese</td><td>-</td><td>-</td><td>63.1</td></tr><tr><td colspan=\"2\">English + Arabic</td><td>-</td><td>60.1</td><td>-</td></tr><tr><td colspan=\"2\">Chinese + Arabic</td><td>51.9</td><td>-</td><td>-</td></tr></table>", | |
| "text": "" | |
| }, | |
| "TABREF6": { | |
| "num": null, | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td>Feature</td><td colspan=\"2\">Chinese Arabic</td></tr><tr><td>Multilingual Word Embedding</td><td>40.2</td><td>53.8</td></tr><tr><td>+ POS Embedding</td><td>40.1</td><td>54.9</td></tr><tr><td>+ Entity Embedding</td><td>40.8</td><td>56.9</td></tr><tr><td>+ Dependency Relation Embedding</td><td>42.5</td><td>58.7</td></tr></table>", | |
| "text": "Event Argument Role Labeling results (F1 %) with perfect event triggers." | |
| }, | |
| "TABREF7": { | |
| "num": null, | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table/>", | |
| "text": "Relation Extraction: Contribution of adding each language-universal feature consecutively for relation extraction on Arabic and Chinese test sets, using the model trained on English (F1 scores (%))." | |
| }, | |
| "TABREF9": { | |
| "num": null, | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td>: Event Extraction: Contributions of adding</td></tr><tr><td>each language-universal feature consecutively for event</td></tr><tr><td>argument role labeling on Arabic and Chinese test</td></tr><tr><td>datasets, using a model trained on English (F1 scores</td></tr><tr><td>(%)).</td></tr></table>", | |
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