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We propose a theoretical framework within which information on the vocabulary of a given corpus can be inferred on the basis of statistical information gathered on that corpus. Inferences can be made on the categories of the words in the…

Computation and Language · Computer Science 2008-10-08 Pascal Vaillant , Richard Nock , Claudia Henry

While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications. One performance bottleneck is predicting the most likely next token…

Computation and Language · Computer Science 2019-07-26 Chunyang Xiao , Christoph Teichmann , Konstantine Arkoudas

The speech signal conveys information on different time scales from short time scale or segmental, associated to phonological and phonetic information to long time scale or supra segmental, associated to syllabic and prosodic information.…

Computation and Language · Computer Science 2016-09-16 Milos Cernak , Afsaneh Asaei , Hervé Bourlard

This article presents a stochastic corpus-based model for generating natural language text. Our model first encodes dependency relations from training data through a feature set, then concatenates these features to produce a new dependency…

Computation and Language · Computer Science 2020-01-14 Elham Seifossadat , Hossein Sameti

This paper explores morpho-syntactic ambiguities for French to develop a strategy for part-of-speech disambiguation that a) reflects the complexity of French as an inflected language, b) optimizes the estimation of probabilities, c) allows…

cmp-lg · Computer Science 2007-05-23 Evelyne Tzoukermann , Dragomir R. Radev , William A. Gale

In this paper we consider a transformer with an $n$-gram structure, such as the one underlying ChatGPT. The transformer provides next word probabilities, which can be used to generate word sequences. We consider methods for computing word…

Machine Learning · Computer Science 2024-03-26 Yuchao Li , Dimitri Bertsekas

In existing literature, while approximate approaches based on Monte-Carlo simulation technique have been proposed to compute the semantics of probabilistic argumentation, how to improve the efficiency of computation without using simulation…

Artificial Intelligence · Computer Science 2017-10-25 Beishui Liao , Kang Xu , Huaxin Huang

A major problem in the study of large language models is to understand their inherent low-dimensional structure. We introduce an approach to study the low-dimensional structure of language models at a model-agnostic level: as sequential…

Machine Learning · Computer Science 2025-10-30 Noah Golowich , Allen Liu , Abhishek Shetty

In this paper, we investigate the use of selectional restriction -- the constraints a predicate imposes on its arguments -- in a language model for speech recognition. We use an un-tagged corpus, followed by a public domain tagger and a…

cmp-lg · Computer Science 2008-02-03 Joerg P. Ueberla

Construction grammar posits that constructions, or form-meaning pairings, are acquired through experience with language (the distributional learning hypothesis). But how much information about constructions does this distribution actually…

Computation and Language · Computer Science 2025-09-26 Joshua Rozner , Leonie Weissweiler , Kyle Mahowald , Cory Shain

Speech enhancement in hearing aids remains a difficult task in nonstationary acoustic environments, mainly because current signal processing algorithms rely on fixed, manually tuned parameters that cannot adapt in situ to different users or…

Acoustic models using probabilistic linear discriminant analysis (PLDA) capture the correlations within feature vectors using subspaces which do not vastly expand the model. This allows high dimensional and correlated feature spaces to be…

Computation and Language · Computer Science 2015-06-23 Liang Lu , Steve Renals

Designers of statistical machine translation (SMT) systems have begun to employ tree-structured translation models. Systems involving tree-structured translation models tend to be complex. This article aims to reduce the conceptual…

Computation and Language · Computer Science 2007-05-23 I. Dan Melamed , Wei Wang

Syntactic structures used to play a vital role in natural language processing (NLP), but since the deep learning revolution, NLP has been gradually dominated by neural models that do not consider syntactic structures in their design. One…

Computation and Language · Computer Science 2023-11-28 Haoyi Wu , Kewei Tu

Common wisdom has it that the bias of stochastic grammars in favor of shorter derivations of a sentence is harmful and should be redressed. We show that the common wisdom is wrong for stochastic grammars that use elementary trees instead of…

Computation and Language · Computer Science 2007-05-23 Rens Bod

Given a string $T$, it is known that its suffix tree can be represented using the compact directed acyclic word graph (CDAWG) with $e_T$ arcs, taking overall $O(e_T+e_{{\overline{T}}})$ words of space, where ${\overline{T}}$ is the reverse…

Data Structures and Algorithms · Computer Science 2017-05-25 Djamal Belazzougui , Fabio Cunial

This document describes a sizable grammar of English written in the TAG formalism and implemented for use with the XTAG system. This report and the grammar described herein supersedes the TAG grammar described in an earlier 1995 XTAG…

Computation and Language · Computer Science 2012-08-27 XTAG Research Group

A deterministic BSP algorithm for constructing the suffix array of a given string is presented, based on a technique which we call accelerated sampling. It runs in optimal O(n/p) local computation and communication, and requires a near…

Distributed, Parallel, and Cluster Computing · Computer Science 2013-02-26 Matthew Felice Pace , Alexander Tiskin

Many complex systems have been shown to share universal properties of organization, such as scale independence, modularity and self-similarity. We borrow tools from statistical physics in order to study structural preferential attachment…

In this thesis, we present two approaches to a rigorous mathematical and algorithmic foundation of quantitative and statistical inference in constraint-based natural language processing. The first approach, called quantitative constraint…

Computation and Language · Computer Science 2007-05-23 Stefan Riezler
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