pub.chive.eprint.submission
Samples
210 randomly sampled records from the AT Protocol firehose
pub.chive.eprint.submission (100 samples)
{
"$type": "pub.chive.eprint.submission",
"abstract": [
{
"$type": "pub.chive.richtext.defs#textItem",
"content": "Some words are harder to learn than others. For instance, action verbs like run and hit are learned earlier than propositional attitude verbs like think and want. One reason think and want might be learned later is that, whereas we can see and hear running and hitting, we can’t see or hear thinking and wanting. Children nevertheless learn these verbs, so a route other than the senses must exist. There is mounting evidence that this route involves, in large part, inferences based on the distribution of syntactic contexts a propositional attitude verb occurs in—a process known as syntactic bootstrapping. This fact makes the domain of propositional attitude verbs a prime proving ground for models of syntactic bootstrapping. With this in mind, this dissertation has two goals: on the one hand, it aims to construct a computational model of syntactic bootstrapping; on the other, it aims to use this model to investigate the limits on the amount of information about propositional attitude verb meanings that can be gleaned from syntactic distributions. I show throughout the dissertation that these goals are mutually supportive. In Chapter 1, I set out the main problems that drive the investigation. In Chapters 2 and 3, I use both psycholinguistic experiments and computational modeling to establish that there is a significant amount of semantic information carried in both participants’ syntactic acceptability judgments and syntactic distributions in corpora. To investigate the nature of this relationship I develop two computational models: (i) a nonnegative model of (semantic-to-syntactic) projection and (ii) a nonnegative model of syntactic bootstrapping. In Chapter 4, I use a novel variant of the Human Simulation Paradigm to show that the information carried in syntactic distribution is actually utilized by (simulated) learners. In Chapter 5, I present a proposal for how to solve a standing problem in how syntactic bootstrapping accounts for certain kinds of cross-linguistic variation. And in Chapter 6, I conclude with some future directions for this work.",
"type": "text"
}
],
"abstractPlainText": "Some words are harder to learn than others. For instance, action verbs like run and hit are learned earlier than propositional attitude verbs like think and want. One reason think and want might be learned later is that, whereas we can see and hear running and hitting, we can’t see or hear thinking and wanting. Children nevertheless learn these verbs, so a route other than the senses must exist. There is mounting evidence that this route involves, in large part, inferences based on the distribution of syntactic contexts a propositional attitude verb occurs in—a process known as syntactic bootstrapping. This fact makes the domain of propositional attitude verbs a prime proving ground for models of syntactic bootstrapping. With this in mind, this dissertation has two goals: on the one hand, it aims to construct a computational model of syntactic bootstrapping; on the other, it aims to use this model to investigate the limits on the amount of information about propositional attitude verb meanings that can be gleaned from syntactic distributions. I show throughout the dissertation that these goals are mutually supportive. In Chapter 1, I set out the main problems that drive the investigation. In Chapters 2 and 3, I use both psycholinguistic experiments and computational modeling to establish that there is a significant amount of semantic information carried in both participants’ syntactic acceptability judgments and syntactic distributions in corpora. To investigate the nature of this relationship I develop two computational models: (i) a nonnegative model of (semantic-to-syntactic) projection and (ii) a nonnegative model of syntactic bootstrapping. In Chapter 4, I use a novel variant of the Human Simulation Paradigm to show that the information carried in syntactic distribution is actually utilized by (simulated) learners. In Chapter 5, I present a proposal for how to solve a standing problem in how syntactic bootstrapping accounts for certain kinds of cross-linguistic variation. And in Chapter 6, I conclude with some future directions for this work.",
"authors": [
{
"affiliations": [
{
"children": [
{
"name": "Department of Linguistics"
}
],
"name": "University of Rochester",
"rorId": "https://ror.org/022kthw22"
}
],
"did": "did:plc:34mbm5v3umztwvvgnttvcz6e",
"handle": "aaronstevenwhite.io",
"name": "Aaron Steven White",
"orcid": "0000-0003-0057-9246",
"order": 1
}
],
"createdAt": "2015-01-01T00:00:00.000Z",
"document": {
"$type": "blob",
"mimeType": "application/pdf",
"ref": {
"$link": "bafkreihs32e6jwnyapbdlej7ykxubjfyl7kh6xx26e2u36c6lumiealoj4"
},
"size": 1919120
},
"documentFormatSlug": "pdf",
"documentFormatUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/3f96bd05-e848-5465-aca9-0a959f778396",
"fieldUris": [
"at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/5bc44344-ffaf-5109-a8d3-b085e1d59a67",
"at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/9cfe6371-0a2c-5aee-8302-f7b170b0d2d8",
"at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/57ad99fd-8f7c-58f9-b9eb-f8f644290336",
"at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/6fe7359e-8b24-5fce-8bd4-0225a10c899e"
],
"keywords": [
"Language Acquisition",
"Semantic Theory",
"Computational Psycholinguistics"
],
"licenseSlug": "all-rights-reserved",
"licenseUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/435ddb5c-9517-51f1-bedd-3d6968ba4678",
"paperTypeSlug": "thesis",
"paperTypeUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/c1cbeda1-24d1-56b3-9fa0-830bf913b339",
"publicationStatusSlug": "published",
"publicationStatusUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/3262e011-81ca-52f3-8b68-7d4c012ef9b7",
"publishedVersion": {
"journal": "University of Maryland",
"publishedAt": "2015-01-01T00:00:00.000Z"
},
"schemaRevision": 3,
"submittedBy": "did:plc:34mbm5v3umztwvvgnttvcz6e",
"title": "Information and Incrementality in Syntactic Bootstrapping",
"version": {
"major": 1,
"minor": 0,
"patch": 0
}
}did:plc:34mbm5v3umztwvvgnttvcz6e | at://did:plc:34mbm5v3umztwvvgnttvcz6e/pub.chive.eprint.submission/3mufmda46eg2t
pub.chive.richtext.defs (nested within pub.chive.eprint.submission) (100 samples)
{
"$type": "pub.chive.eprint.submission",
"abstract": [
{
"$type": "pub.chive.richtext.defs#textItem",
"content": "We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well.",
"type": "text"
}
],
"abstractPlainText": "We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well.",
"authors": [
{
"name": "Rachel Rudinger",
"order": 1
},
{
"affiliations": [
{
"children": [
{
"name": "Department of Linguistics"
}
],
"name": "University of Rochester",
"rorId": "https://ror.org/022kthw22"
}
],
"did": "did:plc:34mbm5v3umztwvvgnttvcz6e",
"handle": "aaronstevenwhite.io",
"name": "Aaron Steven White",
"orcid": "0000-0003-0057-9246",
"order": 2
},
{
"name": "Benjamin Van Durme",
"order": 3
}
],
"conferencePresentation": {
"conferenceLocation": "New Orleans, Louisiana",
"conferenceName": "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
"proceedingsDoi": "10.18653/v1/N18-1067"
},
"createdAt": "2018-06-01T00:00:00.000Z",
"document": {
"$type": "blob",
"mimeType": "application/pdf",
"ref": {
"$link": "bafkreic6kal25srlcbx4osdk222cpdkjkf5iq6csxngtmdmr3cllnkw4ry"
},
"size": 265770
},
"documentFormatSlug": "pdf",
"documentFormatUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/3f96bd05-e848-5465-aca9-0a959f778396",
"fieldUris": [
"at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/f3482b65-fadc-5464-a84f-9bfd6168b291",
"at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/e5fe0691-fa46-5094-b6bc-a31763d7b94a"
],
"keywords": [
"Natural Language Understanding",
"Datasets & Evaluation"
],
"licenseSlug": "CC-BY-4.0",
"licenseUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/fc58b045-e186-5081-b7eb-abc5c47ea8a3",
"paperTypeSlug": "conference-paper",
"paperTypeUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/2ab4132d-8c17-59a6-9d15-8317e01784d4",
"publicationStatusSlug": "published",
"publicationStatusUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/3262e011-81ca-52f3-8b68-7d4c012ef9b7",
"publishedVersion": {
"accessType": "open_access",
"doi": "10.18653/v1/N18-1067",
"journal": "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
"pages": "731–744",
"publishedAt": "2018-06-01T00:00:00.000Z",
"publisher": "Association for Computational Linguistics",
"url": "https://www.aclweb.org/anthology/N18-1067"
},
"repositories": {
"data": [
{
"label": "data",
"platformSlug": "other",
"url": "http://decomp.io/projects/factuality/"
}
]
},
"schemaRevision": 3,
"submittedBy": "did:plc:34mbm5v3umztwvvgnttvcz6e",
"title": "Neural Models of Factuality",
"version": {
"major": 1,
"minor": 0,
"patch": 0
}
}did:plc:34mbm5v3umztwvvgnttvcz6e | at://did:plc:34mbm5v3umztwvvgnttvcz6e/pub.chive.eprint.submission/3mufmcnis7e2s
pub.chive.richtext.facets (nested within pub.chive.eprint.submission) (8 samples)
{
"$type": "pub.chive.eprint.submission",
"abstract": [
{
"$type": "pub.chive.richtext.defs#textItem",
"content": "We introduce event-keyed summarization (EKS), a novel task that marries traditional summarization and document-level event extraction, with the goal of generating a contextualized summary for a specific event, given a document and an extracted event structure. We introduce a dataset for this task, MUCSUM, consisting of summaries of all events in the classic MUC-4 dataset, along with a set of baselines that comprises both pretrained LM standards in the summarization literature, as well as larger frontier models. We show that ablations that reduce EKS to traditional summarization or structure-to-text yield inferior summaries of target events and that MUCSUM is a robust benchmark for this task. Lastly, we conduct a human evaluation of both reference and model summaries, and provide some detailed analysis of the results.",
"facets": [
{
"features": [
{
"$type": "pub.chive.richtext.facets#italic"
}
],
"index": {
"byteEnd": 38,
"byteStart": 13
}
}
],
"type": "text"
}
],
"abstractPlainText": "We introduce event-keyed summarization (EKS), a novel task that marries traditional summarization and document-level event extraction, with the goal of generating a contextualized summary for a specific event, given a document and an extracted event structure. We introduce a dataset for this task, MUCSUM, consisting of summaries of all events in the classic MUC-4 dataset, along with a set of baselines that comprises both pretrained LM standards in the summarization literature, as well as larger frontier models. We show that ablations that reduce EKS to traditional summarization or structure-to-text yield inferior summaries of target events and that MUCSUM is a robust benchmark for this task. Lastly, we conduct a human evaluation of both reference and model summaries, and provide some detailed analysis of the results.",
"authors": [
{
"name": "William Gantt",
"order": 1
},
{
"did": "did:plc:sc3nswzryepptw5vdn2aq3uf",
"handle": "alexmartin314.bsky.social",
"name": "Alexander Martin",
"order": 2
},
{
"name": "Pavlo Kuchmiichuk",
"order": 3
},
{
"affiliations": [
{
"children": [
{
"name": "Department of Linguistics"
}
],
"name": "University of Rochester",
"rorId": "https://ror.org/022kthw22"
}
],
"did": "did:plc:34mbm5v3umztwvvgnttvcz6e",
"handle": "aaronstevenwhite.io",
"name": "Aaron Steven White",
"orcid": "0000-0003-0057-9246",
"order": 4
}
],
"conferencePresentation": {
"conferenceLocation": "Miami, Florida, USA",
"conferenceName": "Findings of the Association for Computational Linguistics: EMNLP 2024"
},
"createdAt": "2024-11-01T12:00:00.000Z",
"document": {
"$type": "blob",
"mimeType": "application/pdf",
"ref": {
"$link": "bafkreid47tx753rb5vtjttvaor6tgw7pkx2y2sijl4n2ycz57bnsem5i6y"
},
"size": 348268
},
"documentFormatSlug": "pdf",
"documentFormatUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/3f96bd05-e848-5465-aca9-0a959f778396",
"fieldUris": [
"at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/a1a96689-b027-53d4-a546-6da7716abfd8",
"at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/f3482b65-fadc-5464-a84f-9bfd6168b291",
"at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/e5fe0691-fa46-5094-b6bc-a31763d7b94a"
],
"keywords": [
"Information Extraction",
"Natural Language Understanding",
"Datasets & Evaluation"
],
"licenseSlug": "CC-BY-4.0",
"licenseUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/fc58b045-e186-5081-b7eb-abc5c47ea8a3",
"paperTypeSlug": "conference-paper",
"paperTypeUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/2ab4132d-8c17-59a6-9d15-8317e01784d4",
"publicationStatusSlug": "published",
"publicationStatusUri": "at://did:plc:5wzpn4a4nbqtz3q45hyud6hd/pub.chive.graph.node/3262e011-81ca-52f3-8b68-7d4c012ef9b7",
"publishedVersion": {
"accessType": "open_access",
"doi": "10.18653/v1/2024.findings-emnlp.431",
"journal": "Findings of the Association for Computational Linguistics: EMNLP 2024",
"pages": "7333–7345",
"publishedAt": "2024-11-01T12:00:00.000Z",
"publisher": "Association for Computational Linguistics",
"url": "https://aclanthology.org/2024.findings-emnlp.431"
},
"repositories": {
"code": [
{
"label": "data",
"platformSlug": "github",
"url": "https://github.com/wgantt/eks"
}
]
},
"schemaRevision": 3,
"submittedBy": "did:plc:34mbm5v3umztwvvgnttvcz6e",
"title": "Event-Keyed Summarization",
"version": {
"major": 1,
"minor": 0,
"patch": 0
}
}did:plc:34mbm5v3umztwvvgnttvcz6e | at://did:plc:34mbm5v3umztwvvgnttvcz6e/pub.chive.eprint.submission/3mufmarej2q2r
pub.chive.richtext.text (nested within pub.chive.eprint.submission) (2 samples)
{
"$type": "pub.chive.eprint.submission",
"abstract": [
{
"$type": "pub.chive.richtext.text",
"content": "A throwaway eprint used to verify that records from other atproto services attach to a paper and render on it. Safe to delete.",
"type": "text"
}
],
"authors": [
{
"did": "did:plc:34mbm5v3umztwvvgnttvcz6e",
"name": "Aaron Steven White",
"order": 1
}
],
"createdAt": "2026-09-04T20:46:45.493081Z",
"document": {
"$type": "blob",
"mimeType": "application/pdf",
"ref": {
"$link": "bafkreidnq2ogmxfudpim4jgg5oneuowmo47odk6zhaxjbgqn5y4qnywjfe"
},
"size": 404
},
"licenseSlug": "CC-BY-4.0",
"licenseUri": "at://did:plc:chive-governance/pub.chive.graph.node/fc58b045-e186-5081-b7eb-abc5c47ea8a3",
"repositories": {
"code": [
{
"label": "Chive on GitHub",
"platformSlug": "github",
"url": "https://github.com/chive-pub/chive"
},
{
"label": "Probe repo on Tangled",
"platformSlug": "tangled",
"recordUri": "at://did:plc:34mbm5v3umztwvvgnttvcz6e/sh.tangled.repo/interop-probe"
}
],
"data": [
{
"label": "MegaIntensionality",
"platformSlug": "other",
"url": "at://did:plc:y4ussuvobbph4mpieqiszqr6/pub.layers.catalog.collection/2c69416698e7bdd5b2ffc9f7"
}
]
},
"submittedBy": "did:plc:34mbm5v3umztwvvgnttvcz6e",
"title": "[TEST] Interop verification probe — safe to delete"
}did:plc:34mbm5v3umztwvvgnttvcz6e | at://did:plc:34mbm5v3umztwvvgnttvcz6e/pub.chive.eprint.submission/3muptixquo527