Articles

We propose one type of TOP (Tangent vector Of the Posterior log-odds) kernel and apply it to text categorization. In a number of categoriza- tion tasks including text categorization, nega- tive examples are usually more common than positive examples and there may be several dif- ferent types of negative examples. …...

This paper reports the results of experiments using memory-based learning to guide a de- terministic dependency parser for unrestricted natural language text. Using data from a small treebank of Swedish, memory-based classifiers for predicting the next action of the parser are constructed. The accuracy of a classifier as such is …...

This paper systematically compares unsuper- vised word sense discrimination techniques that cluster instances of a target word that oc- cur in raw text using both vector and similarity spaces. The context of each instance is repre- sented as a vector in a high dimensional fea- ture space. Discrimination is achieved …...

We describe a statistical approach to semantic role labelling that employs only shallow infor- mation. We use a Maximum Entropy learner, augmented by EM-based clustering to model the fit between a verb and its argument can- didate. The instances to be classified are se- quences of chunks that occur frequently …...

This paper investigates the application of co- training and self-training to word sense disam- biguation. Optimal and empirical parameter se- lection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines co- training with majority voting is introduced, with the effect of …...

The success of supervised learning approaches to word sense disambiguation is largely de- pendent on the features used to represent the context in which an ambiguous word occurs. Previous work has reached mixed conclusions; some suggest that combinations of syntactic and lexical features will perform most effec- tively. However, others …...

This paper considers the task of automatically collecting words with their entity class labels, starting from a small number of labeled ex- amples (‘seed’ words). We show that spec- tral analysis is useful for compensating for the paucity of labeled examples by learning from unlabeled data. The proposed method signif- …...

Automatically deriving semantic structures from text is a challenging task for machine learning. The flat feature representations, usu- ally used in learning models, can only partially describe structured data. This makes difficult the processing of the semantic information that is embedded into parse-trees. In this paper a new kernel for …...

Given a collection of discrete random variables representing outcomes of learned local predic- tors in natural language, e.g., named entities and relations, we seek an optimal global as- signment to the variables in the presence of general (non-sequential) constraints. Examples of these constraints include the type of argu- ments a …...

. Previous research on automatic thesaurus construction most focused on extracting relevant terms for each term of concern from a small-scale and domain-specific corpus. This study emphasizes on utilizing the Web as the rich and dynamic corpus source for term association estimation. In addition to extracting relevant terms, we are …...

. The handling of out-of-vocabulary (OOV) words is one of the key points to a high performance lexical analysis in natural language processing. Among all OOV words, named entities (NE) are the most productive ones. They generally constitute the most meaningful parts of sentences (persons, affairs, time, places, and objects). …...

. In this paper, a noise estimator with rapid adaptation in a variable-level noisy environment is presented. To make noise estimation adapt quickly to highly non-stationary noise environments, a robust voice activity detector (VAD) is utilized in this paper and it depends on the variation of the spectral energy not …...

We present a new approach to intrinsic sum- mary evaluation, based on initial experiments in van Halteren and Teufel (2003), which com- bines two novel aspects: comparison of infor- mation content (rather than string similarity) in gold standard and system summary, mea- sured in shared atomic information units which we …...

This paper proposes a novel method to compile sta- tistical models for machine translation to achieve efficient decoding. In our method, each statistical submodel is represented by a weighted finite-state transducer (WFST), and all of the submodels are ex- panded into a composition model beforehand. Fur- thermore, the ambiguity of …...

This paper introduces an approach to sentiment analysis which uses support vector machines (SVMs) to bring together diverse sources of po- tentially pertinent information, including several fa- vorability measures for phrases and adjectives and, where available, knowledge of the topic of the text. Models using the features introduced are fur- …...

In this paper, we introduce TextRank – a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications. In particular, we propose two innova- tive unsupervised methods for keyword and sentence extraction, and show that the results obtained com- pare favorably …...

Anticipating the availability of large question- answer datasets, we propose a principled, data- driven Instance-Based approach to Question An- swering. Most question answering systems incor- porate three major steps: classify questions accord- ing to answer types, formulate queries for document retrieval, and extract actual answers. Under our ap- proach, strategies …...

Multidocument extractive summarization relies on the concept of sentence centrality to identify the most important sentences in a document. Central- ity is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We are now consid- ering an approach for …...

This paper presents Domain Relevance Estima- tion (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respect to a certain cate- gory. We use a pre-defined set of categories (we call them domains) which have been previously as- sociated to …...

If two translation systems differ differ in perfor- mance on a test set, can we trust that this indicates a difference in true system quality? To answer this question, we describe bootstrap resampling meth- ods to compute statistical significance of test results, and validate them on the concrete example of …...