Articles

The speech fundamental frequency (henceforth F0) contour plays an important role in expressing the affective information of an utterance. The most popular F0 modeling approaches mainly use the concept of separating the F0 contour into a global trend and local variation. For Mandarin, the global trend of the F0 contour …...

There has been considerable interest recently in the processing of affect in spoken interactions. This paper presents an analysis of some conversational speech corpus data showing that the four prosodic characteristics, duration, pitch, power, and voicing all vary significantly according to both interlocutor differences and differences in familiarity over a …...

This paper, based on a selected one hour of expressive speech, is a pilot study on how to use breath segments to get more natural and expressive speech. It mainly deals with the status of when the breath segments occur and how the acoustic features are affected by the speaker’s …...

This paper presents an approach to feature compensation for emotion recognition from speech signals. In this approach, the intonation groups (IGs) of the input speech signals are extracted first. The speech features in each selected intonation group are then extracted. With the assumption of linear mapping between feature spaces in …...

Over the past decade, computational linguists have been striving to design a speech recognition system that is able to identify standard speeches and to accommodate sound variables caused by different individual accents. Furthermore, some speech recognition programs have been able to learn and identify distinctive sound frequencies due to the …...

This paper proposes a new bootstrapping approach to unsupervised part-of-speech induction. In comparison to previous bootstrapping algorithms developed for this problem, our approach aims to improve the quality of the seed clusters by employing seed words that are both distributionally and morphologically reliable. In particular, we present a novel method …...

We introduce a relation extraction method to identify the sentences in biomedical text that indicate an interaction among the protein names mentioned. Our approach is based on the analysis of the paths between two protein names in the dependency parse trees of the sentences. Given two dependency trees, we define …...

We address the problem of smoothing trans- lation probabilities in a bilingual N-gram- based statistical machine translation system. It is proposed to project the bilingual tuples onto a continuous space and to estimate the translation probabilities in this representa- tion. A neural network is used to perform the projection and …...

Recent studies focussed on the question whether less-configurational languages like German are harder to parse than English, or whether the lower parsing scores are an artefact of treebank encoding schemes and data structures, as claimed by K¨ubler et al. (2006). This claim is based on the as- sumption that PARSEVAL …...

We present two machine learning ap- proaches to information extraction from semi-structured documents that can be used if no annotated training data are available, but there does exist a database filled with information derived from the type of docu- ments to be processed. One approach em- ploys standard supervised learning …...

In this paper, we present a three-step mul- tilingual dependency parser based on a deterministic shift-reduce parsing algo- rithm. Different from last year, we sepa- rate the root-parsing strategy as sequential labeling task and try to link the neighbor word dependences via a near neighbor parsing. The outputs of the …...

We describe our submission to the domain adaptation track of the CoNLL07 shared task in the open class for systems using ex- ternal resources. Our main finding was that it was very difficult to map from the annota- tion scheme used to prepare training and de- velopment data to one …...

We present Pro3Gres, a deep-syntactic, fast dependency parser that combines a hand- written competence grammar with proba- bilistic performance disambiguation and that has been used in the biomedical domain. We discuss its performance in the domain adap- tation open submission. We achieve aver- age results, which is partly due to …...

This paper presents an online algorithm for dependency parsing problems. We propose an adaptation of the passive and aggressive online learning algorithm to the dependency parsing domain. We evaluate the proposed algorithms on the 2007 CONLL Shared Task, and report errors analysis. Experimen- tal results show that the system score …...

In the paper we describe a dependency parser that uses exact search and global learning (Crammer et al., 2006) to produce labelled dependency trees. Our system inte- grates the task of learning tree structure and learning labels in one step, using the same set of features for both tasks. During …...

Deterministic parsing has emerged as an ef- fective alternative for complex parsing algo- rithms which search the entire search space to get the best probable parse tree. In this pa- per, we present an online large margin based training framework for deterministic pars- ing using Nivre’s Shift-Reduce parsing al- gorithm. …...

Three versions of the Covington algorithm for non-projective dependency parsing have been tested on the ten different languages for the Multilingual track of the CoNLL- X Shared Task. The results were achieved by using only information about heads and daughters as features to guide the parser which obeys strict incrementality. …...

We describe an incremental parser that was trained to minimize cost over sen- tences rather than over individual parsing ac- tions. This is an attempt to use the advan- tages of the two top-scoring systems in the CoNLL-X shared task. In the evaluation, we present the perfor- mance of the …...

We present a two-stage multilingual de- pendency parsing system submitted to the Multilingual Track of CoNLL-2007. The parser first identifies dependencies using a deterministic parsing method and then labels those dependencies as a sequence labeling problem. We describe the features used in each stage. For four languages with differ- ent …...

We present an adaptation of constraint satis- faction inference (Canisius et al., 2006b) for predicting dependency trees. Three differ- ent classifiers are trained to predict weighted soft-constraints on parts of the complex out- put. From these constraints, a standard weighted constraint satisfaction problem can be formed, the solution to which …...