Unsupervised Morphological Segmentation for Detecting Parkinson’s Disease

AAAI Conferences

The growth of life expectancy entails a rise in prevalence of aging-related neurodegenerative disorders, such as Parkinson's disease. In the ongoing quest to find sensitive behavioral markers of this condition, computerized tools prove particularly promising. Here, we propose a novel method utilizing unsupervised morphological segmentation for accessing morphological properties of a speaker's language. According to our experiments on German, our method can classify patients vs. healthy controls with 81 percent accuracy, and estimate the neurological state of PD patients with Pearson correlation of 0.46 with respect to the unified Parkinson's disease rating scale. Our work is the first study to show that unsupervised morphological segmentation can be used for automatic detection of a neurological disorder.


Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches

Journal of Artificial Intelligence Research

We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. The central assumption of our work is that by combining cues from multiple languages, the structure of each becomes more apparent. We consider two ways of applying this intuition to the problem of unsupervised part-of-speech tagging: a model that directly merges tag structures for a pair of languages into a single sequence and a second model which instead incorporates multilingual context using latent variables. Both approaches are formulated as hierarchical Bayesian models, using Markov Chain Monte Carlo sampling techniques for inference. Our results demonstrate that by incorporating multilingual evidence we can achieve impressive performance gains across a range of scenarios. We also found that performance improves steadily as the number of available languages increases.


Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches

AAAI Conferences

We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. The central assumption of our work is that by combining cues from multiple languages, the structure of each becomes more apparent. We consider two ways of applying this intuition to the problem of unsupervised part-of-speech tagging: a model that directly merges tag structures for a pair of languages into a single sequence and a second model which instead incorporates multilingual context using latent variables. Both approaches are formulated as hierarchical Bayesian models, using Markov Chain Monte Carlo sampling techniques for inference. Our results demonstrate that by incorporating multilingual evidence we can achieve impressive performance gains across a range of scenarios. We also found that performance improves steadily as the number of available languages increases.


Inside Out: Two Jointly Predictive Models for Word Representations and Phrase Representations

AAAI Conferences

Distributional hypothesis lies in the root of most existing word representation models by inferring word meaning from its external contexts. However, distributional models cannot handle rare and morphologically complex words very well and fail to identify some fine-grained linguistic regularity as they are ignoring the word forms. On the contrary, morphology points out that words are built from some basic units, i.e., morphemes. Therefore, the meaning and function of such rare words can be inferred from the words sharing the same morphemes, and many syntactic relations can be directly identified based on the word forms. However, the limitation of morphology is that it cannot infer the relationship between two words that do not share any morphemes. Considering the advantages and limitations of both approaches, we propose two novel models to build better word representations by modeling both external contexts and internal morphemes in a jointly predictive way, called BEING and SEING. These two models can also be extended to learn phrase representations according to the distributed morphology theory. We evaluate the proposed models on similarity tasks and analogy tasks. The results demonstrate that the proposed models can outperform state-of-the-art models significantly on both word and phrase representation learning.


Language Independent Feature Extractor

AAAI Conferences

We propose a new customizable tool, Language Independent Feature Extractor (LIFE), which models the inherent patterns of any language and extracts relevant features of thelanguage. There are two contributions of this work: (1) no labeled data is necessary to train LIFE (It works when a sufficient number of unlabeled documents are given), and (2) LIFE is designed to be applicable to any language. We proved the usefulness of LIFE by experimental results of time information extraction.