The work presented here is an extension of an existing machine learning framework called DARE, a system for learning rules that can be used for extracting instances of relations with different complexity from natural language texts ([XuUsLi07] and [Xu07]). In this paper the term “relation”, in contrast to the intuitive sense of the word, refers to a set of tuples with a certain arity. These tup...

We propose a general approach to modeling semi-supervised learning constraints on unlabeled data. Both traditional supervised classification tasks and many natural semisupervised learning heuristics can be approximated by specifying the desired outcome of walks through a graph of classifiers. We demonstrate the modeling capability of this approach in the task of relation extraction, and experim...

We present a new machine learning approach for 3D-QSAR, the task of predicting binding affinities of molecules to target proteins based on 3D structure. Our approach predicts binding affinity by using regression on substructures discovered by relational learning. We make two contributions to the state-of-the-art. First, we use multiple-instance (MI) regression, which represents a molecule as a ...

Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to quantify the uncertainy of a QSAR prediction is to predict the conditional density of the activity given the structure instead of a point estimate. If a conditional density estimate is available, it is easy to derive pred...

Evaluation of Learning-Based Approaches for Matching Web Data Entities Written by Administrator Thursday, 28 April 2011 10:56 ABSTRACT:Entity matching is a key task for data integration and especially challenging for web data. Effective entity matching typically requires the combination of several match techniques and finding suitable configuration parameters such as similarity thresholds. We i...

QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using statistical learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity. However, predictions from a QSAR model are difficult to assess if their prediction intervals are unknown...

This paper proposes state-of-the-art models for time-event relation extraction (TERE). The models are specifically designed to work effectively with relations that span multiple sentences and paragraphs, i.e., inter-sentence TERE. Our main idea is: (i) to build a computational representation of the context of the two target relation arguments, and (ii) to encode it as structural features in Sup...

This paper investigates the impact of intimate partner violence on participation women in labour market and their access to employment form being a wage worker, self-employed or unpaid family worker. To address possibility endogeneity, especially due simultaneity, between female force participation, we use history violence, both woman her partner, as instrumental variables. Our results provide ...

Customer churn may be a critical issue for banks. The extant literature on statistical and machine learning for customer churn focuses on the problem of correctly predicting that a customer is about to switch bank, while very rarely considers the problem of generating personalized actions to improve the customer retention rate. However, these decisions are at least as critical as the correct id...

Automated discovery and extraction of biological relations from online documents, particularly MEDLINE texts, has become essential and urgent because such literature data are accumulated in a tremendous growth. We present here an ontology-based framework of biological relation extraction system. This framework is unified and able to extract several kinds of relations such as gene-disease, gene-...