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Mark Smith

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2 papers

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AAAI Conference 2016 Conference Paper

Authorship Attribution Using a Neural Network Language Model

Zhenhao Ge
Yufang Sun
Mark Smith

In practice, training language models for individual authors is often expensive because of limited data resources. In such cases, Neural Network Language Models (NNLMs), generally outperform the traditional non-parametric N-gram models. Here we investigate the performance of a feedforward NNLM on an authorship attribution problem, with moderate author set size and relatively limited data. We also consider how the text topics impact performance. Compared with a well-constructed N-gram baseline method with Kneser-Ney smoothing, the proposed method achieves nearly 2. 5% reduction in perplexity and increases author classiﬁcation accuracy by 3. 43% on average, given as few as 5 test sentences. The performance is very competitive with the state of the art in terms of accuracy and demand on test data. The source code, preprocessed datasets, a detailed description of the methodology and results are available at https: //github. com/zge/authorship-attribution.

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NeurIPS Conference 2000 Conference Paper

The Early Word Catches the Weights

Mark Smith
Garrison Cottrell
Karen Anderson

The strong correlation between the frequency of words and their naming latency has been well documented. However, as early as 1973, the Age of Acquisition (AoA) of a word was alleged to be the actual variable of interest, but these studies seem to have been ignored in most of the lit(cid: 173) erature. Recently, there has been a resurgence of interest in AoA. While some studies have shown that frequency has no effect when AoA is con(cid: 173) trolled for, more recent studies have found independent contributions of frequency and AoA. Connectionist models have repeatedly shown strong effects of frequency, but little attention has been paid to whether they can also show AoA effects. Indeed, several researchers have explicitly claimed that they cannot show AoA effects. In this work, we explore these claims using a simple feed forward neural network. We find a sig(cid: 173) nificant contribution of AoA to naming latency, as well as conditions under which frequency provides an independent contribution.

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