Distance-based unsupervised text classification is a method within text classification that leverages the semantic similarity between a label and a text to determine label relevance. This method provides numerous benefits, including fast inference and adaptability to expanding label sets, as opposed to zero-shot, few-shot, and fine-tuned neural networks that require re-training in such cases. In multi-label distance-based classification and information retrieval algorithms, thresholds are required to determine whether a text instance is “similar” to a label or query. Similarity between a text and label is determined in a dense embedding space, usually generated by state-of-the-art sentence encoders. Multi-label classification complicates matters, as a text instance can have multiple true labels, unlike in multi-class or binary classification, where each instance is assigned only one label. We expand upon previous literature on this underexplored topic by thoroughly examining and evaluating the ability of sentence encoders to perform distance-based classification. First, we perform an exploratory study to verify whether the semantic relationships between texts and labels vary across models, datasets, and label sets by conducting experiments on a diverse collection of realistic multi-label text classification (MLTC) datasets. We find that similarity distributions show statistically significant differences across models, datasets and even label sets. We propose a novel method for optimizing label-specific thresholds using a validation set. Our label-specific thresholding method achieves an average improvement of 46% over normalized 0.5 thresholding and outperforms uniform thresholding approaches from previous work by an average of 14%. Additionally, the method demonstrates strong performance even with limited labeled examples.

As social media constitutes a valuable source for data analysis for a wide range of applications, the need for handling such data arises. However, the nonstandard language used on social media poses problems for natural language processing (NLP) tools, as these are typically trained on standard language material. We propose a text normalization approach to tackle this problem. More specifically, we investigate the usefulness of a multimodular approach to account for the diversity of normalization issues encountered in user-generated content (UGC). We consider three different types of UGC written in Dutch (SNS, SMS, and tweets) and provide a detailed analysis of the performance of the different modules and the overall system. We also apply an extrinsic evaluation by evaluating the performance of a part-of-speech tagger, lemmatizer, and named-entity recognizer before and after normalization.

This paper gives an overview of research within the ALADIN project, which aims to develop an assistive vocal interface for people with a physical impairment. In contrast to existing ap-proaches, the vocal interface is trained by the end-user himself, which means it can be used with any vocabulary and grammar, and that it is maximally adapted to the — possibly dysarthric — speech of the user. This paper describes the overall learn-ing framework, the user-centred design and evaluation aspects, database collection and approaches taken to combat problems such as noise and erroneous input. Index Terms: vocal user interface, user-centred design, self-taught learning, speech database, dysarthric speech

This paper introduces research within the ALADIN project, which aims to develop an assistive vocal interface for people with a physical impairment. In contrast to existing approaches, the vocal interface is self-learning which means it can be used with any language, dialect, vocabulary and grammar. The paper describes the overall learning framework, and the two components that will provide vocabulary learning and grammar induction. In addition, the paper describes encouraging results of early implementations of these vocabulary and grammar learning components, applied to recorded sessions of a vocally guided card game, patience. Index Terms: language acquisition, word finding, grammar induction, non-negative matrix factorization, concept tagging

This paper describes a proof-of-the-principle experiment in which maximum entropy learning is used for the automatic induction of shallow morphological features for the resourcescarce Bantu language of Gĩkũyũ. This novel approach circumvents the limitations of typical unsupervised morphological induction methods that employ minimum-edit distance metrics to establish morphological similarity between words. The experimental results show that the unsupervised maximum entropy learning approach compares favorably to those of the established AutoMorphology method. Index Terms: unsupervised learning, morphology, Bantu languages