Topic Coherence

Entity Linking (EL) has received considerable attention in recent years. Given many name mentions in a document, the goal of EL is to predict their referent entities in a knowledge …...

AbstractWord sketches are one-page, automatic, corpus-based summaries of a word's grammatical and collocational behaviour. In this paper we present word sketches for Turkish. Until now, word sketches have been generated …...

Text documents of varying nature (e.g., sum- mary documents written by analysts or pub- lished, scientific papers) often cite others as a means of providing evidence to support a claim, …...

Probabilistic topic models are widely used to discover latent topics in document col- lections, while latent feature word vec- tors have been used to obtain high per- formance in many …...

AbstractProbabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP …...

When evaluating the quality of topics gener- ated by a topic model, the convention is to score topic coherence — either manually or automatically — using the top-N topic words. …...

AbstractTopic models jointly learn topics and document-level topic distribution. Extrinsic evaluation of topic models tends to focus exclusively on topic-level evaluation, e.g. by assessing the coherence of topics. We demonstrate …...

AbstractPrevious research on word embeddings has shown that sparse representations, which can be either learned on top of existing dense embeddings or obtained through model constraints during training time, have …...

AbstractText generation is among the most fundamental tasks in natural language processing. In this paper, we propose a text generation model that learns semantics and structural features simultaneously. This model …...

AbstractEvaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. Automatic metrics such as BLEU correlate weakly with human annotations, resulting in a significant bias across …...

AbstractLeveraging domain knowledge is an effective strategy for enhancing the quality of inferred low-dimensional representations of documents by topic models. In this paper, we develop topic modeling with knowledge graph …...

AbstractShort texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models. To deal with the insufficiency, we propose a generative model that aggregates short texts …...

AbstractTopic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models …...

AbstractTopic modeling is a commonly used method for identifying and extracting topics from a corpus of documents. While several evaluation techniques, such as perplexity and topic coherence, have been developed …...

AbstractTwo types of topic modeling predominate: generative methods that employ probabilistic latent models and clustering methods that identify semantically coherent groups. This paper newly presents UTopic (Unified neural Topic model …...