Латентное распределение Дирихле

We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic posterior inference algorithm for simultaneously …...

Data that has been annotated by linguists is often considered a gold standard on many tasks in the NLP field. However, linguists are expensive so researchers seek automatic techniques that …...

How can the development of ideas in a sci- entific field be studied over time? We ap- ply unsupervised topic modeling to the ACL Anthology to analyze historical trends in …...

The problem of re-ranking initial retrieval re- sults exploring the intrinsic structure of docu- ments is widely researched in information re- trieval (IR) and has attracted a considerable amount of …...

In this paper we model discussions in online political blogs. To do this, we extend Latent Dirichlet Allocation (Blei et al., 2003), in var- ious ways to capture different characteristics …...

This paper addresses the problem of scientific research analysis. We use the topic model Latent Dirichlet Allocation [2] and a novel classifier to classify re- search papers based on topic …...

Models of latent document semantics such as the mixture of multinomials model and La- tent Dirichlet Allocation have received sub- stantial attention for their ability to discover topical semantics in …...

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 establishes a connection be- tween two apparently very different kinds of probabilistic models. Latent Dirich- let Allocation (LDA) models are used as “topic models” to produce a low- …...

Many implementations of Latent Dirichlet Al- location (LDA), including those described in Blei et al. (2003), rely at some point on the removal of stopwords, words which are as- sumed …...

The problem of authorship attribution – at- tributing texts to their original authors – has been an active research area since the end of the 19th century, attracting increased interest …...

Context-dependent word similarity can be measured over multiple cross-cutting dimen- sions. For example, lung and breath are sim- ilar thematically, while authoritative and su- perficial occur in similar syntactic contexts, …...

Latent variable models have the potential to add value to large document collections by discovering interpretable, low-dimensional subspaces. In order for people to use such models, however, they must trust …...

This paper describes the features and the ma- chine learning methods used by Dublin City University (DCU) and SYMANTEC for the WMT 2012 quality estimation task. Two sets of features …...

LDA-based tagging of Web videos This article presents a method for the automatic tagging of youtube videos. The proposed me- thod combines an automatic speech recognition system, that extracts the …...

This study aims to infer the social nature of conversations from their content automat- ically. To place this work in context, our moti- vation stems from the need to understand …...

Extractive multi-document summarization is mostly treated as a sentence ranking problem. Existing graph-based ranking methods for key-sentence extraction usually attempt to compute a global importance score for each sentence under …...

With a few exceptions, extensions to latent Dirichlet allocation (LDA) have focused on the distribution over topics for each document. Much less attention has been given to the un- derlying …...

We address the challenge of automatically generating questions from topics. We consider that each topic is associated with a body of texts containing useful information about the topic. Questions are …...

AbstractDocument zone identification aims to automatically classify sequences of text-spans (e.g. sentences) within a document into predefined zone categories. Current approaches to document zone identification mostly rely on supervised machine …...

The Semantic Textual Similarity (STS) shared task (Agirre et al., 2012) computes the degree of semantic equivalence between two sen- tences.1 We show that a simple unsupervised latent semantics based …...

Recent investigations into grounded models of language have shown that holistic views of language and perception can provide higher performance than independent views. In this work, we improve a two-dimensional …...

Cross-lingual topic modelling has applications in machine translation, word sense disam- biguation and terminology alignment. Multi- lingual extensions of approaches based on la- tent (LSI), generative (LDA, PLSI) as well …...

In this paper we present our systems for cal- culating the degree of semantic similarity be- tween two texts that we submitted to the Se- mantic Textual Similarity task at …...

Various studies highlighted that topic- based approaches give a powerful spo- ken content representation of documents. Nonetheless, these documents may con- tain more than one main theme, and their automatic …...

We describe an algorithm for automatic clas- sification of idiomatic and literal expressions. Our starting point is that words in a given text segment, such as a paragraph, that are …...

Translating text from diverse sources poses a challenge to current machine translation systems which are rarely adapted to structure beyond corpus level. We explore topic adaptation on a diverse data …...

We propose a language modeling (LM) approach incorporating interpolated distanced n-grams in a Dirichlet class language model (DCLM) (Chien and Chueh, 2011) for speech recognition. The DCLM relaxes the bag-of-words …...

Topic models based on latent Dirichlet al- location and related methods are used in a range of user-focused tasks including doc- ument navigation and trend analysis, but evaluation of the …...

Latent Dirichlet allocation (LDA) is a topic model that has been applied to var- ious fields, including user profiling and event summarization on Twitter. When LDA is applied to tweet …...

Word sense induction is an unsupervised task to find and characterize different senses of polyse- mous words. This work investigates two unsupervised approaches that focus on using distribu- tional word …...

With the advent of e-learning, there is a strong demand for tools that help to cre- ate e-learning courses in an automatic or semi-automatic way. While resources for new courses …...

Argumentation mining and stance classifica- tion were recently introduced as interesting tasks in text mining. In this paper, a novel framework for argument tagging based on topic modeling is proposed. …...

This paper studies how to incorporate the ex- ternal word correlation knowledge to improve the coherence of topic modeling. Existing topic models assume words are generated in- dependently and lack …...

This work investigates style and topic aspects of language in online communities: looking at both utility as an identifier of the commu- nity and correlation with community reception of content. …...

AbstractClassifying research grants into useful categories is a vital task for a funding body to give structure to the portfolio for analysis, informing strategic planning and decision-making. Automating this classification …...

AbstractThe paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix. The clustering method combines a Vector Space Models (VSM) and the results of 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 …...

AbstractThe exchangeability assumption in topic models like Latent Dirichlet Allocation (LDA) often results in inferring inconsistent topics for the words of text spans like noun-phrases, which are usually expected to …...

AbstractWhile generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only …...

Automatic caption generation of images has gained significant interest. It gives rise to a lot of interesting image-related applications. For example, it could help in image/video retrieval and management of …...

AbstractDetecting changes within an unfolding event in real time from news articles or social media enables to react promptly to serious issues in public safety, public health or natural disasters. …...

AbstractThis paper presents three hybrid models that directly combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions. Two …...

AbstractProbabilistic topic models such as latent Dirichlet allocation (LDA) are popularly used with Bayesian inference methods such as Gibbs sampling to learn posterior distributions over topic model parameters. We derive …...

AbstractThe introduction of VAE provides an efficient framework for the learning of generative models, including generative topic models. However, when the topic model is a Latent Dirichlet Allocation (LDA) model, …...

AbstractTo scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distributed systems. In this work, we study …...

AbstractTopic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we …...

AbstractThe rapid flow of information and the abundance of text data on the Internet have brought about the urgent demand for the construction of monitoring resources and techniques used for …...

As the import of pork with ractopamine from the United States (US) has stimulated a heated discussion on social media, the purpose of this exploratory study is to assess public …...

AbstractTraditionally, Latent Dirichlet Allocation (LDA) ingests words in a collection of documents to discover their latent topics using word-document co-occurrences. Previous studies show that representing bigrams collocations in the input …...

AbstractSentiment analysis has become a central tool in various disciplines outside of natural language processing. In particular in applied and domain-specific settings with strong requirements for interpretable methods, dictionary-based approaches …...