Nowadays, the multi-label classification is increasingly required in modern categorization systems. It is especially essential in the task of newspaper article topics identification. This paper presents a method based on general topic model normalisation for finding a threshold defining the boundary between the “correct” and the “incorrect” topics of a newspaper article. The proposed method is ...

Correctly choosing the number of topics plays an important role in successfully applying topic models to real world applications. Following the latest tensor decomposition framework by Anandkumar et al., we make the first attempt to provide theoretical analysis on the number of topics under Latent Dirichlet Allocation model. With mild conditions, our method provides accessible information on th...

Objectives Maxillary implant-supported over-denture is a suitable treatment where conventional denture cannot be used. Few studies are available on maxillary over-denture while more are available on mandibular over-denture with no literature referring to attachment selection in maxillary over-dentures. The objective of this literature review was to see attachment selection strategies in maxill...

Most text categorization algorithms represent a document collection as a Bag of Words (BOW).The BOW representation is unable to recognize synonyms from a given term set and unable to recognize semantic relationships between terms. In this paper, we apply the topic-model approach to cluster the words into a set of topics. Words assigned into the same topic are semantically related. Our main goal...

Web forums play a key role in the process of knowledge creation, providing means for users to exchange ideas and to collaborate. However, educational forums, along several others online educational environments, often suffer from topic disruption. Since the contents are mainly produced by participants (in our case learners), one or few individuals might change the course of the discussions. Thu...

This paper presents an unsupervised topic-based language model adaptation method which specializes the standard minimum information discrimination approach by identifying and combining topic-specific features. By acquiring a topic terminology from a thematically coherent corpus, language model adaptation is restrained to the sole probability re-estimation of n-grams ending with some topic-speci...

Topic modeling refers to the task of discovering the underlying thematic structure in a text corpus, where the output is commonly presented as a report of the top terms appearing in each topic. Despite the diversity of topic modeling algorithms that have been proposed, a common challenge in successfully applying these techniques is the selection of an appropriate number of topics for a given co...

Communication is a key capability of autonomous agents in a multiagent system to exchange information about their environment. It requires a naming convention that typically involves a set of predefined names for all objects in the environment, which the agents share and understand. However, when the agents are heterogeneous, highly distributed, and situated in an unknown environment, it is ver...

Topic modelling techniques such as LDA have recently been applied to speech transcripts and OCR output. These corpora may contain noisy or erroneous texts which may undermine topic stability. Therefore, it is important to know how well a topic modelling algorithm will perform when applied to noisy data. In this paper we show that different types of textual noise will have diverse effects on the...

Familia is an open-source toolkit for pragmatic topic modeling in industry. Familia abstracts the utilities of topic modeling in industry as two paradigms: semantic representation and semantic matching. Efficient implementations of the two paradigms are made publicly available for the first time. Furthermore, we provide off-the-shelf topic models trained on large-scale industrial corpora, inclu...