8 research outputs found
Words are Malleable: Computing Semantic Shifts in Political and Media Discourse
Recently, researchers started to pay attention to the detection of temporal
shifts in the meaning of words. However, most (if not all) of these approaches
restricted their efforts to uncovering change over time, thus neglecting other
valuable dimensions such as social or political variability. We propose an
approach for detecting semantic shifts between different viewpoints--broadly
defined as a set of texts that share a specific metadata feature, which can be
a time-period, but also a social entity such as a political party. For each
viewpoint, we learn a semantic space in which each word is represented as a low
dimensional neural embedded vector. The challenge is to compare the meaning of
a word in one space to its meaning in another space and measure the size of the
semantic shifts. We compare the effectiveness of a measure based on optimal
transformations between the two spaces with a measure based on the similarity
of the neighbors of the word in the respective spaces. Our experiments
demonstrate that the combination of these two performs best. We show that the
semantic shifts not only occur over time, but also along different viewpoints
in a short period of time. For evaluation, we demonstrate how this approach
captures meaningful semantic shifts and can help improve other tasks such as
the contrastive viewpoint summarization and ideology detection (measured as
classification accuracy) in political texts. We also show that the two laws of
semantic change which were empirically shown to hold for temporal shifts also
hold for shifts across viewpoints. These laws state that frequent words are
less likely to shift meaning while words with many senses are more likely to do
so.Comment: In Proceedings of the 26th ACM International on Conference on
Information and Knowledge Management (CIKM2017
A Time-based Collective Factorization for Topic Discovery and Monitoring in News
Discovering and tracking topic shifts in news constitutes a new challenge for applications nowadays. Topics evolve, emerge and fade, making it more difficult for the journalist – or the press consumer – to decrypt the news. For instance, the current Syrian chemical crisis has been the starting point of the UN Russian initiative and also the revival of the US France alliance. A topical mapping representing how the topics evolve in time would be helpful to contextualize information. As far as we know, few topic tracking systems can provide such temporal topic connections. In this paper, we introduce a novel framework inspired from Collective Factorization for online topic discovery able to connect topics between different time-slots. The framework learns jointly the topics evolution and their time dependencies. It offers the user the ability to control, through one unique hyper-parameter, the tradeoff between the past accumulated knowledge and the current observed data. We show, on semi-synthetic datasets and on Yahoo News articles, that our method is competitive with state-of-the-art techniques while providing a simple way to monitor topics evolution (including emerging and disappearing topics)
Explainable Recommendation: Theory and Applications
Although personalized recommendation has been investigated for decades, the
wide adoption of Latent Factor Models (LFM) has made the explainability of
recommendations a critical issue to both the research community and practical
application of recommender systems. For example, in many practical systems the
algorithm just provides a personalized item recommendation list to the users,
without persuasive personalized explanation about why such an item is
recommended while another is not. Unexplainable recommendations introduce
negative effects to the trustworthiness of recommender systems, and thus affect
the effectiveness of recommendation engines. In this work, we investigate
explainable recommendation in aspects of data explainability, model
explainability, and result explainability, and the main contributions are as
follows:
1. Data Explainability: We propose Localized Matrix Factorization (LMF)
framework based Bordered Block Diagonal Form (BBDF) matrices, and further
applied this technique for parallelized matrix factorization.
2. Model Explainability: We propose Explicit Factor Models (EFM) based on
phrase-level sentiment analysis, as well as dynamic user preference modeling
based on time series analysis. In this work, we extract product features and
user opinions towards different features from large-scale user textual reviews
based on phrase-level sentiment analysis techniques, and introduce the EFM
approach for explainable model learning and recommendation.
3. Economic Explainability: We propose the Total Surplus Maximization (TSM)
framework for personalized recommendation, as well as the model specification
in different types of online applications. Based on basic economic concepts, we
provide the definitions of utility, cost, and surplus in the application
scenario of Web services, and propose the general framework of web total
surplus calculation and maximization.Comment: 169 pages, in Chinese, 3 main research chapter