In this work, we attempt to detect sentencelevel subjectivity by means of two supervised machine learning approaches: a Fuzzy Control System and Adaptive Neuro-Fuzzy Inference System. Even though these methods are popular in pattern recognition, they have not been thoroughly investigated for subjectivity analysis. We present a novel “Pruned ICF Weighting Coefficient,” which improves the accurac...

We examine existing and novel automatically-derived acoustic metrics that are predictive of speech intelligibility. We hypothesize that the degree of variability in feature space is correlated with the extent of a speaker’s phonemic inventory, their degree of articulatory displacements, and thus with their degree of perceived speech intelligibility. We begin by using fully-automatic F1/F2 forma...

This study uses a novel simulation framework to evaluate whether the time and effort necessary to achieve high recall using active learning is reduced by presenting the reviewer with isolated sentences, as opposed to full documents, for relevance feedback. Under the weak assumption that more time and effort is required to review an entire document than a single sentence, simulation results indi...

We introduce two probabilistic models that can be used to identify elementary discourse units and build sentence-level discourse parse trees. The models use syntactic and lexical features. A discourse parsing algorithm that implements these models derives discourse parse trees with an error reduction of 18.8% over a state-ofthe-art decision-based discourse parser. A set of empirical evaluations...

Theoretical research in the area of machine translation usually involves the search for and creation of an appropriate formalism. An important issue in this respect is the way in which the compositionality of translation is to be defined. In this paper, we will introduce the anaphoric component of the Mimo formalism. It makes the definition and translation of anaphoric relations possible, relat...

The paper presents a preliminary research on possible relations between the syntactic structure and the polarity of a Czech sentence by means of the so-called sentiment analysis of a computer corpus. The main goal of sentiment analysis is the detection of a positive or negative polarity, or neutrality of a sentence (or, more broadly, a text). Most often this process takes place by looking for t...

Sentiment analysis is an automatic way to determine that whether opinions of people about a subject are favorable or unfavorable. One of the most important sub tasks in sentiment analysis is to determine the sequence of words affected by negation. Most of the existing sentiment analysis systems used traditional methods based on static window and punctuation marks to determine the scope of negat...

In this paper, we introduce our system, CopeOpi, for analyzing opinionated information in NTCIR-7 MOAT task’s document collections. We participated in all tasks except opinion target extraction and submitted three runs for both simplified and traditional Chinese sides. For opinion extraction task, our algorithm was based on the bag-ofcharacter methods proposed in NTCIR-6 and considered morpholo...

We present a supervised method for training a sentence level confidence measure on translation output using a humanannotated corpus. We evaluate a variety of machine learning methods. The resultant measure, while trained on a very small dataset, correlates well with human judgments, and proves to be effective on one task based evaluation. Although the experiments have only been run on one MT sy...

In this paper we study the use of sentencelevel dialect identification in optimizing machine translation system selection when translating mixed dialect input. We test our approach on Arabic, a prototypical diglossic language; and we optimize the combination of four different machine translation systems. Our best result improves over the best single MT system baseline by 1.0% BLEU and over a st...