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Linguistic corpus design is a critical concern for building rich annotated corpora useful in different domains of applications. For example, speech technologies such as ASR (Automatic Speech Recogniti...
The evaluation of several tasks in lexical semantics is often limited by the lack of large amounts of manual annotations, not only for training purposes, but also for testing purposes. Word Sense Disa...
Minimum-error-rate training (MERT) is a bottleneck for current development in statistical machine translation because it is limited in the number of weights it can reliably optimize. Building on the w...
We present an open-source frameworkfor large-scale online structured learning.Developed with the flexibility to handle cost-augmented inference problems suchas statistical machine translation (SMT), o...
We present a methodology for extracting subcategorization frames based on an automaticlexical-functional grammar (LFG) f-structure annotation algorithm for the Penn-II and Penn-III Treebanks. We extra...
The need to correct garbled strings arises in many areas of natural language processing. If a dictionary is available that covers all possible input tokens, a natural set of candidates for correcting ...
Exclamatives like What a dump!, Wow!, and Boy, you’ve grown! are, when uttered in context, rich in information about the speaker’s attitudes. Drawing on evidence from about 100, 000 online product rev...
This paper presents a new method for producing a dictionary of subcategorization frames from unlabelled text corpora. It is shown that statistical filtering of the results of a finite state parser run...
Functional and anatomical asymmetries are prevalent features of the human brain, linked to gender, handedness, and cognition. However, little is known about the neurodevelopmental processes involved. ...
In this paper, we describe the core pillars of a large archive of language material recorded worldwide partly about languages that are highly endangered. The bases for the documentation of these langu...
While the natural sciences are used to deal with terabytes and even petabytes of data, such dimensions are new in the domain of linguistics resp. in the humanities. The reasons for this are manifold; ...
In this paper, we investigate the ability of a recently proposed discriminatively trained, multi-level context-dependent acoustic model to adapt to a new speaker in both supervised and unsupervised ad...
In this paper we propose discriminative training of hierarchical acoustic models for large vocabulary continuous speech recognition tasks. After presenting our hierarchical modeling framework, we desc...
In this paper we present a hierarchical large-margin Gaussian mixture modeling framework and evaluate it on the task of phonetic classification. A two-stage hierarchical classifier is trained by alter...
Large-Scale CCG Induction from the Groningen Meaning Bank.

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