State-of-the-art statistical approaches to the Coreference Resolution task rely on sophisticated modeling, but very few (10-20) simple features. In this paper we propose to extend the standard feature set substantially, incorporating more linguistic knowledge. To investigate the usability of linguistically motivated features, we evaluate our system for a variety of machine learners on the stand...

CLaC-CORE, an exhaustive feature combination system ranked 4th among 34 teams in the Semantic Textual Similarity shared task STS 2013. Using a core set of 11 lexical features of the most basic kind, it uses a support vector regressor which uses a combination of these lexical features to train a model for predicting similarity between sentences in a two phase method, which in turn uses all combi...

rust caused by phakopsora pachyrhizi syd. is a major constraint to soybean product in asia. early detection and possibilities of controlling plant diseases by the integration of several image processing methods has been the subject of extensive research. the main contribution of this paper is to present different methodologies for quantitatively detecting soybean rust at each stage of disease d...

A novel method is proposed to improve the duration prediction for HMM based speech synthesis. Based on the decision tree trained by the conventional HTS training method, the duration instances of every leaf node are further clustered into several classes by the K-means clustering method, and the mapping functions between the context features and class labels are trained by CRF. Instead of using...

In this review paper we discuss the various methodologies for plant disease detection. Studies show that relying on pure naked-eye observation of experts to detect and classify diseases can be time consuming and expensive, especially in rural areas and developing countries. So we present fast, automatic, cheap and accurate image processing based solution. Solution is composed of four main phase...

Leaves recognition plays an important role in plant classification. Its key issue lies in whether selected features are stable and have good ability to discriminate different kinds of leaves. In this paper, we propose a novel method of plant classification from leaf image set based on artificial immune system (AIS) and wavelet transforms. AISs are a type of intelligent algorithm; they emulate t...

In the present paper we propose and evaluate a framework for detection and classification of plant leaf/stem diseases using image processing and neural network technique. The images of plant leaves affected by four types of diseases namely early blight, late blight, powdery-mildew and septoria has been considered for study and evaluation of feasibility of the proposed method. The color transfor...

This year, we participated in TRECVID 2010 content-based copy detection task. In this notebook paper we will describe our work in details. Different from last year when we just used SURF (speeded up robust feature) as visual feature, this year we employed a combination of four different features for our rough detection process: global SURF, center SURR, global color correlogram and center corre...