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Speaker-independent system is desirable in many applications where speaker-specific data do not exist. However, if speaker- dependent data are available, the system could be adapted to the specific speaker such …...

In this paper, we describe a fast algorithm for aligning sentences with their translations in a bilingual corpus. Existing efficient algorithms ig- nore word identities and only consider sentence length …...

Improved acoustic modeling can significantly decrease the error rate in large-vocabulary speech recognition. Our approach to the problem is twofold. We first propose a scheme that optimizes the degree of …...

We attemped to improve recognition accuracy by reduc- ing the inadequacies of the lexicon and language model. Specifically we address the following three problems: (1) the best size for the …...

Concerning different approaches to auto- matic PoS tagging: EngCG-2, a constraint- based morphological tagger, is compared in a double-blind test with a state-of-the-art statistical tagger on a common disambigua- tion …...

We describe and experimentally evaluate an efficient method for automatically de- termining small clause boundaries in spon- taneous speech. Our method applies an ar- tificial neural network to information about …...

We present results of probabilistic tag- ging of Czech texts in order to show how these techniques work for one of the highly morphologically ambiguous inflective lan- guages. After description …...

In this paper we present the results of the combination of stochastic and rule-based disambiguation methods applied to Basque languagel. The methods we have used in disambiguation are Constraint Grammar …...

In this paper we present the results of the combination of stochastic and rule-based disambiguation methods applied to Basque languagel. The methods we have used in disambiguation are Constraint Grammar …...

We introduce an algorithm for scope resolution in underspecified semantic representations. Scope pref- erences are suggested on the basis of semantic argu- ment structure. The major novelty of this approach …...

We apply machine learning techniques to classify automatically a set of verbs into lexical semantic classes, based on distributional approximations of diathe- ses, extracted from a very large anno- tated …...

This article describes the current state of syntactic analysis of Estonian using Constraint Grammar. Constraint Gram- mar framework divides parsing into two different modules: morphological disam- biguation and determination of …...

We present a simple architecture for parsing transcribed speech in which an edited-word de- tector first removes such words from the sen- tence string, and then a standard statistical parser …...

This paper presents several practical ways of incorporating linguistic structure into language models. A headword detector is first applied to detect the headword of each phrase in a sentence. A …...

In this paper we investigate the task of automatically identifying the correct argument structure for a set of verbs. The argument structure of a verb allows us to predict the …...

This paper investigates stacking and voting methods for combining strong classifiers like boosting, SVM, and TBL, on the named-entity recognition task. We demonstrate several ef- fective approaches, culminating in a …...

In this paper we demonstrate methods of im- proving both the recall and the precision of au- tomatic methods for extraction of hyponymy (IS A) relations from free text. By …...

In this paper, we address the word alignment problem for statistical machine translation. We aim at creating a sym- metric word alignment allowing for reli- able one-to-many and many-to-one word …...

We present an unsupervised method for labelling the arguments of verbs with their semantic roles. Our bootstrapping algorithm makes initial unam- biguous role assignments, and then iteratively up- dates the …...

We have established a phonotactic lan- guage model as the solution to spoken language identification (LID). In this framework, we define a single set of acoustic tokens to represent the …...

This paper proposes an alignment adaptation approach to improve domain-specific (in-domain) word alignment. The basic idea of alignment adaptation is to use out-of-domain corpus to improve in-domain word alignment results. …...

We describe a method for discriminative training of a language model that makes use of syntactic features. We follow a reranking approach, where a baseline recogniser is used to produce …...

. We build a class-based selection preference sub-model to in- corporate external semantic knowledge from two Chinese electronic se- mantic dictionaries. This sub-model is combined with modifier-head gen- eration sub-model. …...

. Hidden Markov Model (Hmm) is one of the most popular language models. To improve its predictive power, one of Hmm hypotheses, named limited history hypothesis, is usually relaxed. Then …...

This work is concerned with the space of alignments searched by word alignment systems. We focus on situations where word re-ordering is limited by syntax. We present two new alignment …...

. This paper proposes a novel approach to spelling correction. It reranks the output of an existing spelling corrector, Aspell. A discriminative model (Ranking SVM) is employed to improve upon …...

This paper discusses automatic determina- tion of case in Arabic. This task is a ma- jor source of errors in full diacritization of Arabic. We use a gold-standard syntac- tic …...

Many factors are thought to increase the chances of misrecognizing a word in ASR, including low frequency, nearby disfluencies, short duration, and being at the start of a turn. However, …...

In this paper we address the problem of identi- fying a broad range of term variations in Japa- nese web search queries, where these varia- tions pose a particularly thorny …...

We present an unsupervised approach to re- constructing ancient word forms. The present work addresses three limitations of previous work. First, previous work focused on faith- fulness features, which model …...

. Alternating Structure Optimization (ASO) is a recently proposed linear Multi- task Learning algorithm. Although its effective has been verified in both semi-supervised as well as supervised methods, yet they …...

We supplement WordNet entries with infor- mation on the subjectivity of its word senses. Supervised classifiers that operate on word sense definitions in the same way that text classifiers operate …...

A machine translated sentence is seldom completely correct. Confidence measures are designed to detect incorrect words, phrases or sentences, or to provide an esti- mation of the probability of correctness. …...

We define a probabilistic morphological ana- lyzer using a data-driven approach for Syriac in order to facilitate the creation of an annotated corpus. Syriac is an under-resourced Semitic language for …...

: The aim of this study is to build natural language resources for languages with limited resources or minority languages. Manually building these resources is tedious and costly. These natural …...

Open-class semantic lexicon induction is of great interest for current knowledge harvest- ing algorithms. We propose a general frame- work that uses patterns in bootstrapping fash- ion to learn open-class …...

AbstractIn this paper, we present several ways to measure and evaluate the annotation and annotators, proposed and used during the building of the Czech part of the Prague Czech-English Dependency …...

We use an automatic pipeline of word tokenization, stemming, POS tagging, and vocalization to perform real-world Arabic dependency parsing. In spite of the high accuracy on the modules, the very …...

AbstractThe following paper presents a lexical analysis component as implemented in the PANACEA project. The goal is to automatically extract lexicon entries from crawled corpora, in an attempt to use …...

AbstractTACRED is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE). But, even with recent advances in unsupervised pre-training and knowledge enhanced neural RE, models still …...

AbstractDocument and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents, which are commonly used to help downstream tasks such as information retrieval or text …...

In order to study the effectiveness of the current deep learning-based speech recognition models in the speech recognition tasks of Taiwanese Southern Min Recommended Characters and Taiwan Minnanyu Luomazi Pinyin, …...

AbstractThe clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE). However, high-dimensional vectors can encode complex linguistic information which leads to …...

AbstractToday classicists are provided with a great number of digital tools which, in turn, offer possibilities for further study and new research goals. In this paper we explore the idea …...

AbstractThis paper will introduce the use of Automatic Speech Recognition (ASR) technology to process speech content with specific domain. We will use the Conformer end-to-end model as the system architecture, …...

AbstractThe quality of Natural Language Processing (NLP) models is typically measured by the accuracy or error rate of a predefined test set. Because the evaluation and optimization of these measures …...

AbstractIn this paper, we evaluate several Transformer-based language models for Icelandic on four downstream tasks: Part-of-Speech tagging, Named Entity Recognition. Dependency Parsing, and Automatic Text Summarization. We pre-train four types …...

AbstractPhonemes are defined by their relationship to words: changing a phoneme changes the word. Learning a phoneme inventory with little supervision has been a longstanding challenge with important applications to …...