| 1 |
Parallel processing in speech perception with local and global representations of linguistic context
|
|
|
|
In: eLife (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Connecting Documents, Words, and Languages Using Topic Models
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Assessing Composition in Sentence Vector Representations ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Relating lexical and syntactic processes in language: Bridging research in humans and machines
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Guided Probabilistic Topic Models for Agenda-setting and Framing
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Decision Tree-based Syntactic Language Modeling
|
|
|
|
Abstract:
Statistical Language Modeling is an integral part of many natural language processing applications, such as Automatic Speech Recognition (ASR) and Machine Translation. N-gram language models dominate the field, despite having an extremely shallow view of language---a Markov chain of words. In this thesis, we develop and evaluate a joint language model that incorporates syntactic and lexical information in a effort to ``put language back into language modeling.'' Our main goal is to demonstrate that such a model is not only effective but can be made scalable and tractable. We utilize decision trees to tackle the problem of sparse parameter estimation which is exacerbated by the use of syntactic information jointly with word context. While decision trees have been previously applied to language modeling, there has been little analysis of factors affecting decision tree induction and probability estimation for language modeling. In this thesis, we analyze several aspects that affect decision tree-based language modeling, with an emphasis on syntactic language modeling. We then propose improvements to the decision tree induction algorithm based on our analysis, as well as the methods for constructing forest models---models consisting of multiple decision trees. Finally, we evaluate the impact of our syntactic language model on large scale Speech Recognition and Machine Translation tasks. In this thesis, we also address a number of engineering problems associated with the joint syntactic language model in order to make it tractable. Particularly, we propose a novel decoding algorithm that exploits the decision tree structure to eliminate unnecessary computation. We also propose and evaluate an approximation of our syntactic model by word n-grams---the approximation that makes it possible to incorporate our model directly into the CDEC Machine Translation decoder rather than using the model for rescoring hypotheses produced using an n-gram model.
|
|
Keyword:
Computer science; decision tree; syntactic language model
|
|
URL: http://hdl.handle.net/1903/12215
|
|
BASE
|
|
Hide details
|
|
|
| 11 |
Modeling Dependencies in Natural Languages with Latent Variables
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Structured local exponential models for machine translation
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
A Formal Model of Ambiguity and its Applications in Machine Translation
|
|
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Extending Phrase-Based Decoding with a Dependency-Based Reordering Model
|
|
|
|
In: DTIC (2009)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Extending Phrase-Based Decoding with a Dependency-Based Reordering Model
|
|
|
|
BASE
|
|
Show details
|
|
|
| 18 |
COMPUTATIONAL ANALYSIS OF THE CONVERSATIONAL DYNAMICS OF THE UNITED STATES SUPREME COURT
|
|
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
