Inference and Evaluation of the Multinomial Mixture Model for Text Clustering

In this article, we investigate the use of a probabilistic model for unsupervised clustering in text collections. Unsupervised clustering has become a basic module for many intelligent text processing applications, such as information retrieval, text classification or information extraction. The model considered in this contribution consists of a mixture of multinomial distributions over the word counts, each component corresponding to a different theme. We present and contrast various estimation procedures, which apply both in supervised and unsupervised contexts. In supervised learning, this work suggests a criterion for evaluating the posterior odds of new documents which is more statistically sound than the "naive Bayes" approach. In an unsupervised context, we propose measures to set up a systematic evaluation framework and start with examining the Expectation-Maximization (EM) algorithm as the basic tool for inference. We discuss the importance of initialization and the influence of other features such as the smoothing strategy or the size of the vocabulary, thereby illustrating the difficulties incurred by the high dimensionality of the parameter space. We also propose a heuristic algorithm based on iterative EM with vocabulary reduction to solve this problem. Using the fact that the latent variables can be analytically integrated out, we finally show that Gibbs sampling algorithm is tractable and compares favorably to the basic expectation maximization approach

An experimental framework for designing document structure for users' decision making -- An empirical study of recipes

Textual documents need to be of good quality to ensure effective asynchronous communication in remote areas, especially during the COVID-19 pandemic. However, defining a preferred document structure (content and arrangement) for improving lay readers' decision-making is challenging. First, the types of useful content for various readers cannot be determined simply by gathering expert knowledge. Second, methodologies to evaluate the document's usefulness from the user's perspective have not been established. This study proposed the experimental framework to identify useful contents of documents by aggregating lay readers' insights. This study used 200 online recipes as research subjects and recruited 1,340 amateur cooks as lay readers. The proposed framework identified six useful contents of recipes. Multi-level modeling then showed that among the six identified contents, suitable ingredients or notes arranged with a subheading at the end of each cooking step significantly increased recipes' usefulness. Our framework contributes to the communication design via documents

Does document relevance affect the searcher's perception 0f time?

Time plays an essential role in multiple areas of Information Retrieval (IR) studies such as search evaluation, user behavior analysis, temporal search result ranking and query understanding. Especially, in search evaluation studies, time is usually adopted as a measure to quantify users' efforts in search processes. Psychological studies have reported that the time perception of human beings can be affected by many stimuli, such as attention and motivation, which are closely related to many cognitive factors in search. Considering the fact that users' search experiences are affected by their subjective feelings of time, rather than the objective time measured by timing devices, it is necessary to look into the different factors that have impacts on search users' perception of time. In this work, we make a first step towards revealing the time perception mechanism of search users with the following contributions: (1) We establish an experimental research framework to measure the subjective perception of time while reading documents in search scenario, which originates from but is also different from traditional time perception measurements in psychological studies. (2) With the framework, we show that while users are reading result documents, document relevance has small yet visible effect on search users' perception of time. By further examining the impact of other factors, we demonstrate that the effect on relevant documents can also be influenced by individuals and tasks. (3) We conduct a preliminary experiment in which the difference between perceived time and dwell time is taken into consideration in a search evaluation task. We found that the revised framework achieved a better correlation with users' satisfaction feedbacks. This work may help us better understand the time perception mechanism of search users and provide insights in how to better incorporate time factor in search evaluation studies

INEX Tweet Contextualization Task: Evaluation, Results and Lesson Learned

Microblogging platforms such as Twitter are increasingly used for on-line client and market analysis. This motivated the proposal of a new track at CLEF INEX lab of Tweet Contextualization. The objective of this task was to help a user to understand a tweet by providing him with a short explanatory summary (500 words). This summary should be built automatically using resources like Wikipedia and generated by extracting relevant passages and aggregating them into a coherent summary. Running for four years, results show that the best systems combine NLP techniques with more traditional methods. More precisely the best performing systems combine passage retrieval, sentence segmentation and scoring, named entity recognition, text part-of-speech (POS) analysis, anaphora detection, diversity content measure as well as sentence reordering. This paper provides a full summary report on the four-year long task. While yearly overviews focused on system results, in this paper we provide a detailed report on the approaches proposed by the participants and which can be considered as the state of the art for this task. As an important result from the 4 years competition, we also describe the open access resources that have been built and collected. The evaluation measures for automatic summarization designed in DUC or MUC were not appropriate to evaluate tweet contextualization, we explain why and depict in detailed the LogSim measure used to evaluate informativeness of produced contexts or summaries. Finally, we also mention the lessons we learned and that it is worth considering when designing a task

Machine Learning for Readability Assessment and Text Simplification in Crisis Communication: A Systematic Review

In times of social media, crisis managers can interact with the citizens in a variety of ways. Since machine learning has already been used to classify messages from the population, the question is, whether such technologies can play a role in the creation of messages from crisis managers to the population. This paper focuses on an explorative research revolving around selected machine learning solutions for crisis communication. We present systematic literature reviews of readability assessment and text simplification. Our research suggests that readability assessment has the potential for an effective use in crisis communication, but there is a lack of sufficient training data. This also applies to text simplification, where an exact assessment is only partly possible due to unreliable or non-existent training data and validation measures