Aoife Cahill
Biographic Data
| ID | 3844496 |
|---|---|
| NAME | Aoife Cahill |
| GIVEN NAMES | Aoife |
| FAMILY NAME | Cahill |
| SIGNATURE | CAHILL A |
| AFFILIATIONS | Now at the Institut fu ̈r Maschinelle Sprachverarbeitung, Universita ̈t Stuttgart, Germany.. |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2005 |
| LATEST PUBLICATION YEAR | 2016 |
| H-INDEX | 1 |
String Kernels for Native Language Identification
The most common approach in text mining classification tasks is to rely on features like words, part-of-speech tags, stems, or some other high-level linguistic features. Recently, an approach that uses only character p-grams as features has been proposed for the task of native language identification (NLI). The approach obtained state-of-the-art results by combining several string kernels using multiple kernel learning. Despite the fact that the …
Wide-Coverage Deep Statistical Parsing Using Automatic Dependency Structure Annotation
A number of researchers have recently conducted experiments comparing “deep” hand-crafted wide-coverage with “shallow” treebank- and machine-learning-based parsers at the level of dependencies, using simple and automatic methods to convert tree output generated by the shallow parsers into dependencies. In this article, we revisit such experiments, this time using sophisticated automatic LFG f-structure annotation methodologies with surprising res…
Treebank-Based Probabilistic Phrase Structure Parsing
The area of probabilistic phrase structure parsing has been a central and active field in computational linguistics. Stochastic methods in natural language processing, in general, have become very popular as more and more resources become available. One of the main advantages of probabilistic parsing is in disambiguation: it is useful for a parsing system to return a ranked list of potential syntactic analyses for a string. In this article, we in…
Large-Scale Induction and Evaluation of Lexical Resources from the Penn-II and Penn-III Treebanks
We present a methodology for extracting subcategorization frames based on an automatic lexical-functional grammar (LFG) f-structure annotation algorithm for the Penn-II and Penn-III Treebanks. We extract syntactic-function-based subcategorization frames (LFG semantic forms) and traditional CFG category-based subcategorization frames as well as mixed function/category-based frames, with or without preposition information for obliques and particle …
String Kernels for Native Language Identification
The most common approach in text mining classification tasks is to rely on features like words, part-of-speech tags, stems, or some other high-level linguistic features. Recently, an approach that uses only character p-grams as features has been proposed for the task of native language identification (NLI). The approach obtained state-of-the-art results by combining several string kernels using multiple kernel learning. Despite the fact that the …
Large-Scale Induction and Evaluation of Lexical Resources from the Penn-II and Penn-III Treebanks
We present a methodology for extracting subcategorization frames based on an automatic lexical-functional grammar (LFG) f-structure annotation algorithm for the Penn-II and Penn-III Treebanks. We extract syntactic-function-based subcategorization frames (LFG semantic forms) and traditional CFG category-based subcategorization frames as well as mixed function/category-based frames, with or without preposition information for obliques and particle …
Wide-Coverage Deep Statistical Parsing Using Automatic Dependency Structure Annotation
A number of researchers have recently conducted experiments comparing “deep” hand-crafted wide-coverage with “shallow” treebank- and machine-learning-based parsers at the level of dependencies, using simple and automatic methods to convert tree output generated by the shallow parsers into dependencies. In this article, we revisit such experiments, this time using sophisticated automatic LFG f-structure annotation methodologies with surprising res…
Treebank-Based Probabilistic Phrase Structure Parsing
The area of probabilistic phrase structure parsing has been a central and active field in computational linguistics. Stochastic methods in natural language processing, in general, have become very popular as more and more resources become available. One of the main advantages of probabilistic parsing is in disambiguation: it is useful for a parsing system to return a ranked list of potential syntactic analyses for a string. In this article, we in…
String Kernels for Native Language Identification
The most common approach in text mining classification tasks is to rely on features like words, part-of-speech tags, stems, or some other high-level linguistic features. Recently, an approach that uses only character p-grams as features has been proposed for the task of native language identification (NLI). The approach obtained state-of-the-art results by combining several string kernels using multiple kernel learning. Despite the fact that the …
Artificial Intelligence (4 works) · Computer Science (4 works) · Natural language processing (4 works) · Natural Language Processing Techniques (4 works) · Topic Modeling (4 works) · Annotation (2 works) · Mathematics (2 works) · Parsing (2 works) · Programming language (2 works) · Set (abstract data type (2 works)