Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task

The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. State-of-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines

Descriptive document clustering via discriminant learning in a co-embedded space of multilevel similarities

Descriptive document clustering aims at discovering clusters of semantically interrelated documents together with meaningful labels to summarize the content of each document cluster. In this work, we propose a novel descriptive clustering framework, referred to as CEDL. It relies on the formulation and generation of 2 types of heterogeneous objects, which correspond to documents and candidate phrases, using multilevel similarity information. CEDL is composed of 5 main processing stages. First, it simultaneously maps the documents and candidate phrases into a common co‐embedded space that preserves higher‐order, neighbor‐based proximities between the combined sets of documents and phrases. Then, it discovers an approximate cluster structure of documents in the common space. The third stage extracts promising topic phrases by constructing a discriminant model where documents along with their cluster memberships are used as training instances. Subsequently, the final cluster labels are selected from the topic phrases using a ranking scheme using multiple scores based on the extracted co‐embedding information and the discriminant output. The final stage polishes the initial clusters to reduce noise and accommodate the multitopic nature of documents. The effectiveness and competitiveness of CEDL is demonstrated qualitatively and quantitatively with experiments using document databases from different application fields

Bayesian Hidden Topic Markov Models

Recent developments in topic modeling for text corpora have incorporated Markov models in the latent space to better learn contextual content. Known as the Hidden Topic Markov Model (HTMM), this natural extension of probabilistic mixture models relaxes the bag-of-words assumption of the foundational latent Dirichlet allocation topic model by allowing the discrete latent variables, or topics, to follow a special first-order Markov process. Parameter estimation is performed using an expectation-maximization (EM) algorithm with fixed dimensionality of the topic space (Gruber, Rosen-Zvi, and Weiss 2007). I fully derive the state space and EM algorithm for the HTMM. I then extend the Hidden Topic Markov Model (HTMM) into a fully Bayesian framework using a Gibbs sampler. The necessary full conditional distributions are derived and a Gibbs sampling algorithm proposed. I implement both the HTMM EM algorithm (Gruber, Rosen-Zvi, and Weiss 2007) and the HTMM Gibbs sampling algorithm in the R and C++ programming languages. The performance of both inferential algorithms is evaluated on twelve simulated data sets and on a collection of proceedings from the Conference on Neural Information Processing Systems (NIPS). The results suggest that the Gibbs sampling algorithm provides better recovery of the topic space than a combination of the EM and Viterbi algorithms. Parameter estimation is comparable using point estimates with both algorithms. The convergence of the Gibbs sampler is studied and is reliable for reasonably large data sets. Evaluation of both algorithms on the NIPS corpus suggests that the HTMM is better able to handle polysemy than LDA and provides coherent and contiguous topics. Predictive accuracy measured by perplexity is better on training and test documents using the HTMM than using LDA on the NIPS corpus. Introducing Markovian dynamics in topical space provides better topical segmentation of a corpus and increased predictive accuracy for unseen documents

Deep learning with knowledge graphs for fine-grained emotion classification in text

This PhD thesis investigates two key challenges in the area of fine-grained emotion detection in textual data. More specifically, this work focuses on (i) the accurate classification of emotion in tweets and (ii) improving the learning of representations from knowledge graphs using graph convolutional neural networks.The first part of this work outlines the task of emotion keyword detection in tweets and introduces a new resource called the EEK dataset. Tweets have previously been categorised as short sequences or sentence-level sentiment analysis, and it could be argued that this should no longer be the case, especially since Twitter increased its allowed character limit. Recurrent Neural Networks have become a well-established method to classify tweets over recent years, but have struggled with accurately classifying longer sequences due to the vanishing and exploding gradient descent problem. A common technique to overcome this problem has been to prune tweets to a shorter sequence length. However, this also meant that often potentially important emotion carrying information, which is often found towards the end of a tweet, was lost (e.g., emojis and hashtags). As such, tweets mostly face also problems with classifying long sequences, similar to other natural language processing tasks. To overcome these challenges, a multi-scale hierarchical recurrent neural network is proposed and benchmarked against other existing methods. The proposed learning model outperforms existing methods on the same task by up to 10.52%. Another key component for the accurate classification of tweets has been the use of language models, where more recent techniques such as BERT and ELMO have achieved great success in a range of different tasks. However, in Sentiment Analysis, a key challenge has always been to use language models that do not only take advantage of the context a word is used in but also the sentiment it carries. Therefore the second part of this work looks at improving representation learning for emotion classification by introducing both linguistic and emotion knowledge to language models. A new linguistically inspired knowledge graph called RELATE is introduced. Then a new language model is trained on a Graph Convolutional Neural Network and compared against several other existing language models, where it is found that the proposed embedding representations achieve competitive results to other LMs, whilst requiring less pre-training time and data. Finally, it is investigated how the proposed methods can be applied to document-level classification tasks. More specifically, this work focuses on the accurate classification of suicide notes and analyses whether sentiment and linguistic features are important for accurate classification