Topic Model

In this paper wc discuss the use of discourse con- text in spoken dialogue systems and argue that the knowledge of the domain, modelled with the help of dialogue topics …...

In this paper, we propose a novel topic model based on incorporating dictionary definitions. Traditional topic models treat words as surface strings without assuming predefined knowledge about word mean- ing. …...

We present a novel topic modelling-based methodology to track emerging events in microblogs such as Twitter. Our topic model has an in-built update mechanism based on time slices and implements …...

With the rapid growth of social media, Twitter has become one of the most widely adopted platforms for people to post short and instant message. On the one hand, people …...

The problem of learning language models from large text corpora has been widely stud- ied within the computational linguistic com- munity. However, little is known about the performance of these …...

In this paper we present an unsupervised ap- proach to relational information extraction. Our model partitions tuples representing an observed syntactic relationship between two named entities (e.g., “X was born …...

We propose a novel unsupervised extractive approach for summarizing online reviews by ex- ploiting review helpfulness ratings. In addition to using the helpfulness ratings for review-level filtering, we suggest using …...

We describe a topic model based approach for selectional preference. Using the topic fea- tures generated by an LDA model on the ex- tracted predicate-arguments over the Chinese Gigaword corpus, …...

The difficult language in Electronic Health Records (EHRs) presents a challenge to patients’ understanding of their own con- ditions. One approach to lowering the barrier is to provide tailored patient …...

AbstractWe introduce Sprite, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, …...

Suicide is one of major public health problems worldwide. Traditionally, suicidal ideation is assessed by surveys or interviews, which lacks of a real-time assessment of personal men- tal state. Online …...

Document collections often have links be- tween documents—citations, hyperlinks, or revisions—and which links are added is often based on topical similarity. To model these intuitions, we introduce a new topic …...

AbstractTopics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a topic with a …...

AbstractLatent Dirichlet Allocation (LDA) and its variants have been widely used to discover latent topics in textual documents. However, some of topics generated by LDA may be noisy with irrelevant …...

AbstractLanguage models are typically applied at the sentence level, without access to the broader document context. We present a neural language model that incorporates document context in the form of …...

AbstractTraditional topic models are insufficient for topic extraction in social media. The existing methods only consider text information or simultaneously model the posts and the static characteristics of social media. …...

AbstractThe bigger the corpus, the more topics it can potentially support. To truly make full use of massive text corpora, a topic model inference algorithm must therefore scale efficiently in …...

AbstractModeration of reader comments is a significant problem for online news platforms. Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper. …...

AbstractTopic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or …...