Topic Models

Given a movie comment, does it contain a spoiler? A spoiler is a comment that, when disclosed, would ruin a surprise or reveal an important plot detail. We study automatic …...

This paper describes the application of so-called topic models to selectional pref- erence induction. Three models related to Latent Dirichlet Allocation, a proven method for modelling document-word co- occurrences, are …...

It is well known that parsing accuracy suf- fers when a model is applied to out-of-domain data. It is also known that the most benefi- cial data to parse a …...

Topic models have been used extensively as a tool for corpus exploration, and a cottage in- dustry has developed to tweak topic models to better encode human intuitions or to …...

Topic models have great potential for help- ing users understand document corpora. This potential is stymied by their purely un- supervised nature, which often leads to top- ics that are …...

Topic models are increasingly being used for text analysis tasks, often times replacing ear- lier semantic techniques such as latent seman- tic analysis. In this paper, we develop a novel …...

We describe a new probabilistic model for extracting events between major polit- ical actors from news corpora. Our un- supervised model brings together famil- iar components in natural language pro- …...

This paper describes the XLING system partici- pation in SemEval-2013 Crosslingual Word Sense Disambiguation task. The XLING system introduces a novel approach to skip the sense disambiguation step by matching …...

A key challenge for computational conver- sation models is to discover latent struc- ture in task-oriented dialogue, since it pro- vides a basis for analysing, evaluating, and building conversational systems. …...

This paper studies the idea of remov- ing low-frequency words from a corpus, which is a common practice to reduce computational costs, from a theoretical standpoint. Based on the assumption …...

Topic models, an unsupervised technique for inferring translation domains improve machine translation quality. However, pre- vious work uses only the source language and completely ignores the target language, which can …...

AbstractInferring the information structure of scientific documents is useful for many NLP applications. Existing approaches to this task require substantial human effort. We propose a framework for constraint learning that …...

Effective text classification requires experts to annotate data with labels; these training data are time-consuming and expensive to obtain. If you know what labels you want, active learning can reduce …...

Computationally modeling the evolution of science by tracking how scientific top- ics rise and fall over time has important implications for research funding and pub- lic policy. However, little is …...

Conventional topic models are ineffec- tive for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence pat- terns. In …...

AbstractModels work best when they are optimized taking into account the evaluation criteria that people care about. For topic models, people often care about interpretability, which can be approximated using …...

AbstractMost previous work in unsupervised semantic modeling in the presence of metadata has assumed that our goal is to make latent dimensions more correlated with metadata, but in practice the …...

AbstractTopic models are typically evaluated with respect to the global topic distributions that they generate, using metrics such as coherence, but without regard to local (token-level) topic assignments. Token-level assignments …...

AbstractIn the last few years, a number of successful approaches have emerged that are able to adequately model various aspects of natural language. In particular, language models based on neural …...

AbstractWork on bias in hate speech typically aims to improve classification performance while relatively overlooking the quality of the data. We examine selection bias in hate speech in a language …...

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 models are a useful analysis tool to uncover the underlying themes within document collections. The dominant approach is to use probabilistic topic models that posit a generative story, but …...

Topic models are effective in capturing the la- tent semantics of large-scale textual data while existing methods are normally designed and evaluated on balanced corpora. However, it contradicts the fact …...

AbstractWith the increasing impact of Natural Language Processing tools like topic models in social science research, the experimental rigor and comparability of models and datasets has come under scrutiny. Especially …...

AbstractWhen developing topic models, a critical question that should be asked is: How well will this model work in an applied setting? Because standard performance evaluation of topic interpretability uses …...