Detecting Erroneous Sentences using Automatically Mined Sequential Patterns

Mining Sequential Patterns and Tree Patterns to Detect Erroneous Sentences

An important application area of detecting erroneous sentences is to provide feedback for writers of English as a Second Language. This problem is difficult since both erroneous and correct sentences are diversified. In this paper, we propose a novel approach to identifying erroneous sentences. We first mine labeled tree patterns and sequential patterns to characterize both erroneous and correc...

Identifying Protein-Protein Interaction Sentences

As the amount of biological research literature increases, finding information is becoming a daunting task. Since machine learning techniques could alleviate this problem, we propose a machine learning framework to identify protein-protein interaction sentences from research papers. This machine learning technique is one of the basic components needed to automatically extract biological informa...

Mining the Strongest Patterns in Medical Sequential Data

Sequential data represent an important source of automatically mined and potentially new medical knowledge. They can originate in various ways. Within the presented domain they come from a longitudinal preventive study of atherosclerosis – the data consist of series of long-term observations recording the development of risk factors and associated conditions. The intention is to identify freque...

Machine Translation Detection from Monolingual Web-Text

We propose a method for automatically detecting low-quality Web-text translated by statistical machine translation (SMT) systems. We focus on the phrase salad phenomenon that is observed in existing SMT results and propose a set of computationally inexpensive features to effectively detect such machine-translated sentences from a large-scale Web-mined text. Unlike previous approaches that requi...

EFL Teachers’ Reconstruction of Student-Writers’ Intentions in Erroneous Sentences: The Role of Context