The authors’ objectives were to see “whether a ‘lite’ approach of this kind could yield reasonable performance, before pursuing possibilities that relied on ‘deeper’ NLP analysis methods”, “which of the features would contribute positively to system performance” and “if any [ machine learning ] approach was better suited to the TempEval tasks

For us, the results of (Hepple et al., 2007) are interesting as they allow for a straightforward evaluation of our adaptation efforts, since the same machine learning implementations can be used with the Portuguese data, and then compared to their results.

Table 2: Performance of several machine learning algorithms on the English TempEval-l training data, with cross-validation.

Supervised machine learning approaches are pervasive in the tasks of temporal information processing.

Even when the best performing systems in these competitions are symbolic, there are machine learning solutions with results close to their performance.

In the TERN2004 competition (aimed at identifying and normalizing temporal expressions), a symbolic system performed best, but since then machine learning solutions, such as (Ahn et al., 2007), have appeared that obtain similar results.

We will argue that the automatic identification of generic expressions should be cast as a machine learning problem instead of a rule-based approach, as there is (i) no transparent marking of genericity in English (as in most other European languages) and (ii) the phenomenon is highly context dependent.

In this paper, we build on insights from formal semantics to establish a corpus-based machine learning approach for the automatic classification of generic expressions.

In our view, these observations call for a corpus-based machine learning approach that is able to capture a variety of factors indicating genericity in combination and in context.

For example, as shown in Figure l, with the background knowledge that both Learning and Graphical models are the topics related to Machine learning, while Machine learning is the sub domain of Computer science, a human can easily determine that the two Michael Jordan in the 15t and 4th observations represent the same person.

1) Michael Jordan is a in Machine learning

Machine learning Probability Theory V

Statistics Basketball Machine learning 0.5 8 0.00 MVP 0.00 0.45

Nonparametric Bayesian inference, where the inference task involves learning not just the values of a finite vector of parameters but which parameters are relevant, has been the focus of intense research in machine learning recently.

Over the last few years there has been considerable interest in Bayesian inference for complex hierarchical models both in machine learning and in computational linguistics.

This paper establishes a theoretical connection between two very different kinds of probabilistic models: Probabilistic Context-Free Grammars (PCFGs) and a class of models known as Latent Dirichlet Allocation (Blei et al., 2003; Griffiths and Steyvers, 2004) models that have been used for a variety of tasks in machine learning .

plication of CRFs, which are a major advance of recent years in machine learning .

As is the case with many machine learning methods, no strong guidance is available for choosing values for these parameters.

Over the years, various machine learning methods have been applied to the hyphenation task.

Training We obtained phrase-based salience scores using a supervised machine learning algorithm.

We obtain these scores from the output of a supervised machine learning algorithm that predicts for each phrase whether it should be included in the highlights or not (see Section 5 for details).

Let fi denote the salience score for phrase i, determined by the machine learning algorithm, and li is its length in tokens.