Since the release of the large discourse-level annotation of the Penn Discourse Treebank (PDTB), research work has been carried out on certain subtasks of this annotation, such as disambiguating discourse connectives and classifying Explicit or Implicit relations. We see a need to construct a full parser on top of these subtasks and propose a way to evaluate the parser. In this work, we have designed and developed an end-to-end discourse parser-to-parse free texts in the PDTB style in a fully data-driven approach. The parser consists of multiple components joined in a sequential pipeline architecture, which includes a connective classifier, argument labeler, explicit classifier, non-explicit classifier, and attribution span labeler. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies the sense of the relation between each pair of arguments. For the identified relations, the parser also determines the attribution spans, if any, associated with them. We introduce novel approaches to locate and label arguments, and to identify attribution spans. We also significantly improve on the current state-of-the-art connective classifier. We propose and present a comprehensive evaluation from both component-wise and error-cascading perspectives, in which we illustrate how each component performs in isolation, as well as how the pipeline performs with errors propagated forward. The parser gives an overall system F1 score of 46.80 percent for partial matching utilizing gold standard parses, and 38.18 percent with full automation.

This study examines the ability of a semantic space model to represent the meaning of noun compounds such as ‘information gathering’ or ‘heart disease.’ For a semantic space model to compute the meaning and the attributional similarity (or semantic relatedness) for unfamiliar noun compounds that do not occur in a corpus, the vector for a noun compound must be computed from the vectors of its constituent words using vector composition algorithms. Six composition algorithms (i.e., centroid, multiplication, circular convolution, predication, comparison, and dilation) are compared in terms of the quality of the computation of the attributional similarity for English and Japanese noun compounds. To evaluate the performance of the computation of the similarity, this study uses three tasks (i.e., related word ranking, similarity correlation, and semantic classification), and two types of semantic spaces (i.e., latent semantic analysis-based and positive pointwise mutual information-based spaces). The result of these tasks is that the dilation algorithm is generally most effective in computing the similarity of noun compounds, while the multiplication algorithm is best suited specifically for the positive pointwise mutual information-based space. In addition, the comparison algorithm works better for unfamiliar noun compounds that do not occur in the corpus. These findings indicate that in general a semantic space model, and in particular the dilation, multiplication, and comparison algorithms have sufficient ability to compute the attributional similarity for noun compounds.

This paper presents morpheme-based language models developed for Amharic (a morphologically rich Semitic language) and their application to a speech recognition task. A substantial reduction in the out of vocabulary rate has been observed as a result of using subwords or morphemes. Thus a severe problem of morphologically rich languages has been addressed. Moreover, lower perplexity values have been obtained with morpheme-based language models than with word-based models. However, when comparing the quality based on the probability assigned to the test sets, word-based models seem to fare better. We have studied the utility of morpheme-based language models in speech recognition systems and found that the performance of a relatively small vocabulary (5k) speech recognition system improved significantly as a result of using morphemes as language modeling and dictionary units. However, as the size of the vocabulary increases (20k or more) the morpheme-based systems suffer from acoustic confusability and did not achieve a significant improvement over a word-based system with an equivalent vocabulary size even with the use of higher order (quadrogram) n-gram language models.

The growing number of publicly available information sources makes it impossible for individuals to keep track of all the various opinions on one topic. The goal of our Fuzzy Believer system presented in this paper is to extract and analyze statements of opinion from newspaper articles. Beliefs are modeled using the fuzzy set theory, applied after Natural Language Processing-based information extraction. The Fuzzy Believer models a human agent, deciding what statements to believe or reject based on a range of configurable strategies.