Native Language Identification (NLI) is a task aimed at determining the native language (L1) of learners of second language (L2) on the basis of their written texts. To date, research on NLI has focused on relatively small corpora. We apply NLI to the recently released EFCamDat corpus which is not only multiple times larger than previous L2 corpora but also provides longitudinal data at several...

Attempts to profile authors based on their characteristics, including native language, have drawn attention in recent years, via several approaches using machine learning with simple features. In this paper we investigate the potential usefulness to this task of contrastive analysis from second language acquistion research, which postulates that the (syntactic) errors in a text are influenced b...

The task of Native Language Identification (NLI) is typically solved with machine learning methods, and systems make use of a wide variety of features. Some preliminary studies have been conducted to examine the effectiveness of individual features, however, no systematic study of feature interaction has been carried out. We propose a function to measure feature independence and analyze its eff...

By extending the least squares based iterative (LSI) method, this paper presents a decomposition based LSI (D-LSI) algorithm for identifying linear-in-parameters systems and an intervalvarying D-LSI algorithm for handling the identification problems of missing-data systems. The basic idea is to apply the hierarchical identification principle to decompose the original system into two fictitious ...

An efficient, and intuitive algorithm is presented for the identification of speakers from a long dataset (like YouTube long discussion, Cocktail party recorded audio or video).The goal of automatic speaker identification is to identify the number of different speakers and prepare a model for that speaker by extraction, characterization and speaker-specific information contained in the speech s...

The assumption on the mass error distribution of fragment ions plays a crucial role in peptide identification by tandem mass spectra. Previous mass error models are the simplistic uniform or normal distribution with empirically set parameter values. In this paper, we propose a more accurate mass error model, namely conditional normal model, and an iterative parameter learning algorithm. The new...

A No-Reset Iterative Learning Control (NRILC) system is an Iterative Learning Control (ILC) system where the plant is not reset at the beginning of each iteration. We compare NRILC with ILC and repetitive control systems in terms of structure. We apply this new scheme to discrete-time, LTI, SISO plants but the approach can be extended to linear time-varying and MIMO plants. We show that an NRIL...

This paper proposes an automatic method for vertebra localization, labeling, and segmentation in multi-slice Magnetic Resonance (MR) images. Prior work in this area on MR images mostly requires user interaction while our method is fully automatic. Cubic intensity-based features are extracted from image voxels. A deep learning approach is used for simultaneous localization and identification of ...