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Full-Text Articles in Computational Linguistics
Acoustic Classification Of Focus: On The Web And In The Lab, Jonathan Howell, Mats Rooth, Michael Wagner
Acoustic Classification Of Focus: On The Web And In The Lab, Jonathan Howell, Mats Rooth, Michael Wagner
Department of Linguistics Faculty Scholarship and Creative Works
We present a new methodological approach which combines both naturally-occurring speech harvested on the web and speech data elicited in the laboratory. This proof-of-concept study examines the phenomenon of focus sensitivity in English, in which the interpretation of particular grammatical constructions (e.g., the comparative) is sensitive to the location of prosodic prominence. Machine learning algorithms (support vector machines and linear discriminant analysis) and human perception experiments are used to cross-validate the web-harvested and lab-elicited speech. Results con rm the theoretical predictions for location of prominence in comparative clauses and the advantages using both web-harvested and lab-elicited speech. The most robust …
Evaluating And Automating The Annotation Of A Learner Corpus, Alexandr Rosen, Jirka Hana, Barbora Stindlova, Anna Feldman
Evaluating And Automating The Annotation Of A Learner Corpus, Alexandr Rosen, Jirka Hana, Barbora Stindlova, Anna Feldman
Department of Linguistics Faculty Scholarship and Creative Works
The paper describes a corpus of texts produced by non-native speakersof Czech. We discuss its annotation scheme, consisting of three interlinked tiers,designed to handle a wide range of error types present in the input. Each tier correctsdifferent types of errors; links between the tiers allow capturing errors in word orderand complex discontinuous expressions. Errors are not only corrected, but alsoclassified. The annotation scheme is tested on a data set including approx. 175,000words with fair inter-annotator agreement results. We also explore the possibility ofapplying automated linguistic annotation tools (taggers, spell checkers and grammarcheckers) to the learner text to support or even …
Prosodylab-Aligner: A Tool For Forced Alignment Of Laboratory Speech, Kyle Gorman, Jonathan Howell, Michael Wagner
Prosodylab-Aligner: A Tool For Forced Alignment Of Laboratory Speech, Kyle Gorman, Jonathan Howell, Michael Wagner
Department of Linguistics Faculty Scholarship and Creative Works
The Penn Forced Aligner automates the alignment process using the Hidden Markov Model Toolkit (HTK). The core of Prosodylab-Aligner is align.py, a script which performs acoustic model training and alignment. This script automates calls to HTK and SoX, an open-source command-line tool which is capable of resampling audio. The included README file provides instructions for installing HTK and SoX on Linux and Mac OS X, and can also be run on Windows. During training, the model is initialized with flat-start monophones, which are then submitted to a single round of model estimation. Then, a tied-state 'small pause' model is inserted …
Tagset Design, Inflected Languages, And N-Gram Tagging, Anna Feldman
Tagset Design, Inflected Languages, And N-Gram Tagging, Anna Feldman
Department of Linguistics Faculty Scholarship and Creative Works
This paper explores the relationship between the tagset design and linguistic properties of inflected languages for the task of morphosyntactic tagging. Some information theoretic measures and statistics on these languages are reported which show, unsurprisingly, that the tagsets for morphologically rich languages are larger than tagsets for English and the average tag/token ambiguity is higher. The surprising outcome of the experiments is that for Catalan, Czech, Polish, Portuguese,and Russian – which are considered to be “word order” free languages (to various degrees) – the knowledge about the preceding tag reduces the uncertainty about the tag in question if the detailed …