Matches in ScholarlyData for { ?s ?p ?o. }
- 762 type InProceedings.
- 762 label "A Corpus for Cross-Document Co-reference".
- 762 sameAs 762.
- 762 abstract "This paper describes a newly created text corpus of news articles that has been annotated for cross-document co-reference. Being able to robustly resolve references to entities across document boundaries will provide a useful capability for a variety of tasks, ranging from practical information retrieval applications to challenging research in information extraction and natural language understanding. This annotated corpus is intended to encourage the development of systems that can more accurately address this problem. A manual annotation tool was developed that allowed the complete corpus to be searched for likely co-referring entity mentions. This corpus of 257K words links mentions of co-referent people, locations and organizations (subject to some additional constraints). Each of the documents had already been annotated for within-document co-reference by the LDC as part of the ACE series of evaluations. The annotation process was bootstrapped with a string-matching-based linking procedure, and we report on some of initial experimentation with the data. The cross-document linking information will be made publicly available.".
- 762 hasAuthorList authorList.
- 762 hasTopic Linguistics.
- 762 isPartOf proceedings.
- 762 keyword "Anaphora, Coreference".
- 762 keyword "Authoring tools, proofing".
- 762 keyword "Corpus (creation, annotation, etc.)".
- 762 title "A Corpus for Cross-Document Co-reference".
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- item-1 hasContent david-day.
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- item-2 hasContent janet-hitzeman.
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- item-3 hasContent michael-wick.
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- item-4 hasContent keith-crouch.
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- item-5 hasContent massimo-poesio.
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- 763 creator alessandro-lenci.
- 763 creator barbara-mcgillivray.
- 763 creator simonetta-montemagni.
- 763 creator vito-pirrelli.
- 763 type InProceedings.
- 763 label "Unsupervised Acquisition of Verb Subcategorization Frames from Shallow-Parsed Corpora".
- 763 sameAs 763.
- 763 abstract "In this paper, we reported experiments of unsupervised automatic acquisition of Italian and English verb subcategorization frames (SCFs) from general and domain corpora. The proposed technique operates on syntactically shallow-parsed corpora on the basis of a limited number of search heuristics not relying on any previous lexico-syntactic knowledge about SCFs. Although preliminary, reported results are in line with state-of-the-art lexical acquisition systems. The issue of whether verbs sharing similar SCFs distributions happen to share similar semantic properties as well was also explored by clustering verbs that share frames with the same distribution using the Minimum Description Length Principle (MDL). First experiments in this direction were carried out on Italian verbs with encouraging results.".
- 763 hasAuthorList authorList.
- 763 hasTopic Linguistics.
- 763 isPartOf proceedings.
- 763 keyword "Acquisition, Machine Learning".
- 763 keyword "Corpus (creation, annotation, etc.)".
- 763 keyword "Lexicon, lexical database".
- 763 title "Unsupervised Acquisition of Verb Subcategorization Frames from Shallow-Parsed Corpora".
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- item-1 hasContent alessandro-lenci.
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- item-2 hasContent barbara-mcgillivray.
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- item-3 hasContent simonetta-montemagni.
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- item-4 hasContent vito-pirrelli.
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- 764 creator guergana-savova.
- 764 creator james-masanz.
- 764 creator karin-schuler.
- 764 creator philip-ogren.
- 764 creator vinod-kaggal.
- 764 type InProceedings.
- 764 label "System Evaluation on a Named Entity Corpus from Clinical Notes".
- 764 sameAs 764.
- 764 abstract "This paper presents the evaluation of the dictionary look-up component of Mayo Clinic s Information Extraction system. The component was tested on a corpus of 160 free-text clinical notes which were manually annotated with the named entity disease. This kind of clinical text presents many language challenges such as fragmented sentences and heavy use of abbreviations and acronyms. The dictionary used for this evaluation was a subset of SNOMED-CT with semantic types corresponding to diseases/disorders without any augmentation. The algorithm achieves an F-score of 0.56 for exact matches and F-scores of 0.76 and 0.62 for right and left-partial matches respectively. Machine learning techniques are currently under investigation to improve this task.".
- 764 hasAuthorList authorList.
- 764 hasTopic Linguistics.
- 764 isPartOf proceedings.
- 764 keyword "Corpus (creation, annotation, etc.)".
- 764 keyword "Named Entity recognition".
- 764 keyword "Tools, systems, applications".
- 764 title "System Evaluation on a Named Entity Corpus from Clinical Notes".
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- item-1 hasContent karin-schuler.
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