5.3 Parameters and Learning Algorithm

Selection of learning algorithm and its algorithm-specific parameters were done as follows.

Since each dataset has only 140 examples, the computation time of each learning algorithm is negligible.

The standard classification process is to find in an auxiliary corpus a set of patterns in which a given training word pair co-appears, and use pattern-word pair co-appearance statistics as features for machine learning algorithms .

Various learning algorithms have been used for relation classification.

Freely available tools like Weka (Witten and Frank, 1999) allow easy experimentation with common learning algorithms (Hendrickx et al., 2007).

By combining a supervised large margin loss with an unsupervised least squares loss, a dis-criminative, convex, semi-supervised learning algorithm can be obtained that is applicable to large-scale problems.

Unlike previous proposed approaches, we introduce a convex objective for the semi-supervised learning algorithm by combining a convex structured SVM loss and a convex least square loss.

Supervised learning algorithms still represent the state of the art approach for inferring dependency parsers from data (McDonald et al., 2005a; McDonald and Pereira, 2006; Wang et al., 2007).

Unfortunately, although significant recent progress has been made in the area of semi-supervised learning, the performance of semi-supervised learning algorithms still fall far short of expectations, particularly in challenging real-world tasks such as natural language parsing or machine translation.

The basic idea is to bootstrap a supervised learning algorithm by alternating between inferring the missing label information and retraining.

However, normal machine learning algorithms work on attribute-value vectors, which only allows the representation of atomic proposition.

This requirement motivates our use of Inductive Logic Programming (ILP), a learning algorithm capable of inferring logic programs.

Default parameters were applied for all the other settings in ALEPH as well as other learning algorithms used in the experiments.

Even worse, the number of mentions in an entity is not fixed, which would result in variant-length feature vectors and make trouble for normal machine learning algorithms .

Based on the training instances, a binary classifier can be generated using any discriminative learning algorithm .