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Latent Semantic Indexing (LSI) approach provides a promising solution to overcome the language barrier between queries and documents, but unfortunately the high dimensions of the training matrix is computationally prohibitive …...

This paper1 presents our approach to the problem of single sentence summarisation. We investigate the use of Singular Value Decomposition (SVD) to guide the generation of a summary towards the …...

We propose an approach to summarization exploiting both lexical information and the output of an automatic anaphoric re- solver, and using Singular Value Decom- position (SVD) to identify the main …...

. This paper describes an automatic summarization approach that constructs a summary by extracting the significant sentences. The approach takes advantage of the cooccurrence relationships between terms only in the …...

. This paper presents a new document clustering method based on frequent co-occurring words. We first employ the Singular Value Decomposition, and then group the words into clusters called word …...

An algorithm based on the Generalized Hebbian Algorithm is described that allows the singular value decomposition of a dataset to be learned based on single observation pairs presented seri- ally. …...

Following (Blitzer et al., 2006), we present an application of structural correspondence learning to non-projective dependency pars- ing (McDonald et al., 2005). To induce the correspondences among dependency edges from …...

This work describes the University of the Basque Country system (UBC-ALM) for lexical sample and all-words WSD subtasks of SemEval-2007 task 17, where it per- formed in the second and …...

In this article a multiclassifier approach for word sense disambiguation (WSD) prob- lems is presented, where a set of k-NN classifiers is used to predict the category (sense) of each …...

Latent Semantic Analysis (LSA) is based on the Singular Value Decompo- sition (SVD) of a term-by-document matrix for identifying relationships among terms and documents from co- occurrence patterns. Among the …...

In this paper we explore robustness and domain adaptation issues for Word Sense Disambiguation (WSD) using Singular Value Decomposition (SVD) and unlabeled data. We focus on the semi-supervised do- main …...

In this paper, we propose a novel way to include unsupervised feature selection meth- ods in probabilistic taxonomy learning mod- els. We leverage on the computation of logistic regression to …...

In this paper we investigate how to identify initiation-response pairs in asynchronous, multi-threaded, multi-party conversations. We formulate the task of identifying initia- tion-response pairs as a pairwise ranking problem. A …...

AbstractWe present a framework for analyzing what the state in RNNs remembers from its input embeddings. We compute the gradients of the states with respect to the input embeddings and …...

AbstractEntity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous …...

AbstractThis study evaluates whether model-based Collaborative Filtering (CF) algorithms, which have been extensively studied and widely used to build recommender systems, can be used to predict which common nouns a …...

AbstractLarge pretrained multilingual language models (ML-LMs) have shown remarkable capabilities of zero-shot cross-lingual transfer, without direct cross-lingual supervision. While these results are promising, follow-up works found that, within the multilingual …...

AbstractSingular value decomposition (SVD) is one of the most popular compression methods that approximate a target matrix with smaller matrices. However, standard SVD treats the parameters within the matrix with …...

AbstractThis paper introduces a new method, SummVD, for automatic unsupervised extractive summarization. This method is based on singular value decomposition, a linear method in the number of words, in order …...