We propose an approach that biases machine translation systems toward relevant translations based on topic-specific contexts, where topics are induced in an unsupervised way using topic models; this can be thought of as inducing subcorpora for adaptation without any human annotation. We use these topic distributions to compute topic-dependent lexical weighting probabilities and directly incorpo...

Count data arises for example in bioinformatics or analysis of text documents represented as word count vectors. With several data sets available from related sources, exploiting their similarities by transfer learning can improve models compared to modeling sources independently. We introduce a Bayesian generative transfer learning model which represents similarity across document collections ...

Latent Dirichlet Allocation (LDA) is a topic modeling tool that automatically discovers topics from a large collection of documents. It is one of the most popular text analysis tools currently in use. In practice however, the topics discovered by LDA do not always make sense to end users. In this extended abstract, we propose an active learning framework that interactively and iteratively acqui...

Topic sentiment joint model is an extended model which aims to deal with the problem of detecting sentiments and topics simultaneously from online reviews. Most of existing topic sentiment joint modeling algorithms infer resulting distributions from the co-occurrence of words. But when the training corpus is short and small, the resulting distributions might be not very satisfying. In this pape...

The clusters of review sentences on the viewpoints from the products’ evaluation can be applied to various use. The topic models, for example Unigram Mixture (UM), can be used for this task. However, there are two problems. One problem is that topic models depend on the randomly-initialized parameters and computation results are not consistent. The other is that the number of topics has to be s...

This paper introduces a hybrid model that combines a neural network with a latent topic model. The neural network provides a lowdimensional embedding for the input data, whose subsequent distribution is captured by the topic model. The neural network thus acts as a trainable feature extractor while the topic model captures the group structure of the data. Following an initial pretraining phase ...

In this paper we describe our participation in the SIGHAN 2010 Task3 (Person Name Disambiguation) and detail our approaches. Person Name Disambiguation is typically viewed as an unsupervised clustering problem where the aim is to partition a name’s contexts into different clusters, each representing a real world people. The key point of Clustering is the similarity measure of context, which dep...

This paper presents a formalization for Topic Maps (TM). We first simplify TMRM, the current ISO standard proposal for a TM reference model and then characterize topic map instances. After defining a minimal merging operator for maps we propose a formal foundation for a TM query language. This path expression language allows us to navigate through given topic maps and to extract information. We...

Biterm Topic Model (BTM) is designed to model the generative process of the word co-occurrence patterns in short texts such as tweets. However, two aspects of BTM may restrict its performance: 1) user individualities are ignored to obtain the corpus level words co-occurrence patterns; and 2) the strong assumptions that two co-occurring words will be assigned the same topic label could not disti...

Self-disclosure, the act of revealing oneself to others, is an important social behavior that contributes positively to intimacy and social support from others. It is a natural behavior, and social scientists have carried out numerous quantitative analyses of it through manual tagging and survey questionnaires. Recently, the flood of data from online social networks (OSN) offers a practical way...