Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most supervised methods consider the summarization task as a twoclass classification problem and classify each sentence individually without leveraging the relationship among sentences. The unsupervised methods use heuristic rules to select the most informative sentences in...

In American Sign Language (ASL) the structure of signed sentences is conveyed by grammatical markers which are represented by facial feature movements and head motions. Without recovering grammatical markers, a sign language recognition system cannot fully reconstruct a signed sentence. However, this problem has been largely neglected in the literature. In this paper, we propose to use a 2-laye...

We describe a combination of a statistical and symbolic approaches for automated scoring of student utterances according to their semantic content. The proposed semantic classifier overcomes the limitations of bag-of-wordsmethods by mapping natural language sentences into predicate representations and matching them against the automatically generated deductive closure of the domain givens, bugg...

A complete automatic speech segmentation technique has been studied in order to eliminate the need for manually segmented sentences. The goal is to fix the phoneme boundaries using only the speech waveform and the phonetic sequence of the sentences. The phonetic boundaries are established using a Dynamic Time Warping algorithm that uses the a posteriori probabilities of each phonetic unit given...

Many of previous research have proven that the usage of rhetorical relations is capable to enhance many applications such as text summarization, question answering and natural language generation. This work proposes an approach that expands the benefit of rhetorical relations to address redundancy problem for cluster-based text summarization of multiple documents. We exploited rhetorical relati...

In this paper, we proposed a novel approach for Tibetan word segmentation using the conditional random field. We reformulate the segmentation as a syllable tagging problem. The approach labels each syllable with a word-internal position tag, and combines syllable(s) into words according to their tags. As there is no public available Tibetan word segmentation corpus, the training corpus is gener...

Reranking modules of conventional parsers make use of either probabilistic weights linked to the production rules or just hand crafted rules to choose the best possible parse. Other proposals make use of the topology of the parse trees and lexical features to reorder the parsing results. In this work, a new reranking approach is presented. There are two main novelties introduced in this paper: ...

There is much interest in providing proba-· bilistic semantics for defaults but most ap­ proaches seem to suffer from one of two prob­ lems: either they require numbers, a problem defaults were intended to avoid, or they gen­ erate peculiar side effects. Rather than provide semantics to defaults, we address the original problem that defaults were intended to solve: that of reasoning un­ der unc...

Probabilistic accounts of language processing can be psychologically tested by comparing word-reading times (RT) to the conditional word probabilities estimated by language models. Using surprisal as a linking function, a significant correlation between unlexicalized surprisal and RT has been reported (e.g., Demberg and Keller, 2008), but success using lexicalized models has been limited. In th...

This paper argues that the word order of adverbials can be captured only when the causal clauses are analyzed as focus. Previous studies on Chinese causal sentences usually treat the causal clauses on a par with other types of adverbial clauses. This means that they are considered as topics and predicted to precede the main clauses. However, this account does not hold for the causal clauses. Ac...