Matches in ScholarlyData for { ?s ?p ?o. }
- 227 abstract "This paper presents a context sensitive spell checking system that uses mixed trigram models, and introduces a new empirically grounded method for building confusion sets. The proposed method has been implemented, tested, and evaluated in terms of coverage, precision, and recall. The results show that the method is effective.".
- 227 hasAuthorList authorList.
- 227 hasTopic Linguistics.
- 227 isPartOf proceedings.
- 227 keyword "Authoring tools, proofing".
- 227 keyword "Language modelling".
- 227 keyword "Tools, systems, applications".
- 227 title "I saw TREE trees in the park: How to Correct Real-Word Spelling Mistakes".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent davide-fossati.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent barbara-di-eugenio.
- item-2 previous item-1.
- 228 creator dafydd-gibbon.
- 228 creator jolanta-bachan.
- 228 type InProceedings.
- 228 label "An Automatic Close Copy Speech Synthesis Tool for Large-Scale Speech Corpus Evaluation".
- 228 sameAs 228.
- 228 abstract "The production of rich multilingual speech corpus resources on a large scale is a requirement for many linguistic, phonetic and technological tasks, in both research and application domains. It is also time-consuming and therefore expensive. The human component in the resource creation process is also prone to inconsistencies, a situation frequently documented in cross-transcriber consistency studies. In the present case, corpora of three languages were to be evaluated and corrected: (1) Polish, a large automatically annotated and manually corrected single-speaker TTS unit-selection corpus in the BOSS Label File (BLF) format, (2) German and (3) English, the second and third being manually annotated multi-speaker story-telling learner corpora in Praat TextGrid format. A method is provided for supporting the evaluation and correction of time-aligned annotations for the three corpora by permitting a rapid audio screening of the annotations by an expert listener for the detection of perceptually conspicuous systematic or isolated errors in the annotations. The criterion for perceptual conspicuousness was provided by converting the annotation formats into the interface format required by the MBROLA speech synthesiser. The audio screening procedure is complementary to other methods of corpus evaluation and does not replace them.".
- 228 hasAuthorList authorList.
- 228 hasTopic Linguistics.
- 228 isPartOf proceedings.
- 228 keyword "Corpus (creation, annotation, etc.)".
- 228 keyword "Speech resource/database".
- 228 keyword "Speech synthesis, Text-to-speech systems".
- 228 title "An Automatic Close Copy Speech Synthesis Tool for Large-Scale Speech Corpus Evaluation".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent dafydd-gibbon.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent jolanta-bachan.
- item-2 previous item-1.
- 229 creator james-h-martin.
- 229 creator sara-klingenstein.
- 229 creator steven-bethard.
- 229 creator william-corvey.
- 229 type InProceedings.
- 229 label "Building a Corpus of Temporal-Causal Structure".
- 229 sameAs 229.
- 229 abstract "While recent corpus annotation efforts cover a wide variety of semantic structures, work on temporal and causal relations is still in its early stages. Annotation efforts have typically considered either temporal relations or causal relations, but not both, and no corpora currently exist that allow the relation between temporals and causals to be examined empirically. We have annotated a corpus of 1000 event pairs for both temporal and causal relations, focusing on a relatively frequent construction in which the events are conjoined by the word and . Temporal relations were annotated using an extension of the BEFORE and AFTER scheme used in the TempEval competition, and causal relations were annotated using a scheme based on connective phrases like and as a result . The annotators achieved 81.2% agreement on temporal relations and 77.8% agreement on causal relations. Analysis of the resulting corpus revealed some interesting findings, for example, that over 30% of CAUSAL relations do not have an underlying BEFORE relation. The corpus was also explored using machine learning methods, and while model performance exceeded all baselines, the results suggested that simple grammatical cues may be insufficient for identifying the more difficult temporal and causal relations.".
- 229 hasAuthorList authorList.
- 229 hasTopic Linguistics.
- 229 isPartOf proceedings.
- 229 keyword "Corpus (creation, annotation, etc.)".
- 229 keyword "Other".
- 229 keyword "Semantics".
- 229 title "Building a Corpus of Temporal-Causal Structure".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasLastItem item-4.
- item-1 type ListItem.
- item-1 hasContent steven-bethard.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent william-corvey.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent sara-klingenstein.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent james-h-martin.
- item-4 previous item-3.
- 23 creator hinrich-schuetze.
- 23 creator michaela-atterer.
- 23 type InProceedings.
- 23 label "An Inverted Index for Storing and Retrieving Grammatical Dependencies".
- 23 sameAs 23.
- 23 abstract "Web count statistics gathered from search engines have been widely used as a resource in a variety of NLP tasks. For some tasks, however, the information they exploit is not fine-grained enough. We propose an inverted index over grammatical relations as a fast and reliable resource to access more general and also more detailed frequency information. To build the index, we use a dependency parser to parse a large corpus. We extract binary dependency relations, such as he-subj-say ( he is the subject of say ) as index terms and construct the index using publicly available open-source indexing software. The unit we index over is the sentence. The index can be used to extract grammatical relations and frequency counts for these relations. The framework also provides the possibility to search for partial dependencies (say, the frequency of he occurring in subject position), words, strings and a combination of these. One possible application is the disambiguation of syntactic structures.".
- 23 hasAuthorList authorList.
- 23 hasTopic Linguistics.
- 23 isPartOf proceedings.
- 23 keyword "Information Extraction, Information Retrieval".
- 23 keyword "LR Infrastructures and Architectures".
- 23 title "An Inverted Index for Storing and Retrieving Grammatical Dependencies".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent michaela-atterer.
- item-1 next item-2.
- item-2 type ListItem.