In this paper, we present an approach to primarily achieve the semantic interpretation and the region retrieval for an attentive region in a color image. The main components of the system include image feature extraction, indexing process, as well as linguistic inference rules construction and semantic description. Based on these features, each of attentive regions in an image can be described ...

We compare the relative utility of different automatically computable linguistic feature sets for modeling student learning in computer dialogue tutoring. We use the PARADISE framework (multiple linear regression) to build a learning model from each of 6 linguistic feature sets: 1) surface features, 2) semantic features, 3) pragmatic features, 4) discourse structure features, 5) local dialogue ...

In this paper, we consider the problem of sentence difficulty analysis from various angles. Past works have endeavored to design deterministic scoring algorithms depending only on semantic and syntactic information. We propose instead not only to hire local feature space representing individual sentence with its syntactic and semantic structure, but also to consider global distributional differ...

Different structural features of human language change at different rates and thus exhibit different temporal stabilities. Existing methods of linguistic stability estimation depend upon the prior genealogical classification of the world’s languages into language families; these methods result in unreliable stability estimates for features which are sensitive to horizontal transfer between fami...

This paper describes the use of a low-dimensional vector representation of sentence acoustics to control the output of a feed-forward deep neural network text-to-speech system on a sentence-by-sentence basis. Vector representations for sentences in the training corpus are learned during network training along with other parameters of the model. Although the network is trained on a frame-by-fram...

In this paper we present experiments for global polarity classification task of Spanish tweets for TASS 2015 challenge. In our methodology, tweets representation is focused on linguistic and polarity features such as lemmatized words, filter of content words, rules of negation, among others. In addition, different transformations are used (LDA, LSI, and TF-IDF) and combined with a SVM classifie...

An iterative method to select suitable features in an industrial fabric defect recognition context is proposed in this paper. It combines a global feature selection method based on the Choquet integral and a fuzzy linguistic rule classifier. The experimental study shows the wanted behaviour of this approach: the feature number decreases whereas the recognition rate increases. Thus, the number o...

A hierarchical hidden Markov model (HHMM) based approach of product named entity recognition (NER) from Chinese free text is presented in this paper. Characteristics and challenges in product NER is also investigated and analyzed deliberately compared with general NER. Within a unified statistical framework, the approach we proposed is able to make probabilistically reasonable decisions to a gl...

In this paper, we describe our participation in the fourth shared task (NLPCC-ICCPOL 2016 Shared Task 4) on the stance detection in Chinese Micro-blogs (subtask A). Different from ordinary features, we explore four linguistic features including lexical features, morphology features, semantic features and syntax features in Chinese micro-blogs in stance classifier, and get a good performance, wh...

This paper explores a new approach to speech recognition in which sub-word units are modeled in terms of linguistic features. Specifically, we have adopted a scheme of modeling separately the manner and place of articulation for these units. A novelty of our work is the use of a generalized definition of place of articulation that enables us to map both vowels and consonants into a common lingu...