Modern search engines retrieve results mainly based on the keyword matching techniques, and thus fail to answer analytical queries like "apps with more than 1 billion monthly active users" or "population growth of the US from 2015 to 2019", which requires numerical reasoning or aggregating results from multiple web pages. Such analytical queries are very common in the data analysis area, the expected results would be structured tables or charts. In most cases, these structured results are not available or accessible, they scatter in various text sources. In this work, we build AnaSearch, a search system to support analytical queries, and return structured results that can be visualized in the form of tables or charts. We collect and build structured quantitative data from the unstructured text on the web automatically. With AnaSearch, data analysts could easily derive insights for decision making with keyword or natural language queries. Specifically, we build AnaSearch under the COVID-19 news data, which makes it easy to compare with manually collected structured data.

Combining data content with visual embellishments, infographics can effectively deliver messages in an engaging and memorable manner. Various authoring tools have been proposed to facilitate the creation of infographics. However, creating a professional infographic with these authoring tools is still not an easy task, requiring much time and design expertise. Therefore, these tools are generally not attractive to casual users, who are either unwilling to take time to learn the tools or lacking in proper design expertise to create a professional infographic. In this paper, we explore an alternative approach: to automatically generate infographics from natural language statements. We first conducted a preliminary study to explore the design space of infographics. Based on the preliminary study, we built a proof-of-concept system that automatically converts statements about simple proportion-related statistics to a set of infographics with pre-designed styles. Finally, we demonstrated the usability and usefulness of the system through sample results, exhibits, and expert reviews.

Qian Liu, Bei Chen, Haoyan Liu, Jian-Guang Lou, Lei Fang, Bin Zhou, Dongmei Zhang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Haoyan Liu, Lei Fang, Qian Liu, Bei Chen, Jian-Guang Lou, Zhoujun Li. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

For online reviews, sentiment explanations refer to the sentences that may suggest detailed reasons of sentiment, which are very important for applications in review mining like opinion summarization. In this paper, we address the problem of ranking sentiment explanations by formulating the process as two subproblems: sentence informativeness ranking and structural sentiment analysis. Tractable inference in joint prediction is performed through dual decomposition. Preliminary experiments on publicly available data demonstrate that our approach obtains promising performance.

In sentiment analysis, aspect-level review analysis has been an important task because it can catalogue, aggregate, or summarize various opinions according to a product's properties. In this paper, we explore a new concept for aspect-level review analysis, latent sentiment explanations, which are defined as a set of informative aspect-specific sentences whose polarities are consistent with that of the review. In other words, sentiment explanations best represent a review in terms of both aspect and polarity. We formulate the problem as a structure learning problem, and sentiment explanations are modeled with latent variables. Training samples are automatically identified through a set of pre-defined aspect signature terms (i.e., without manual annotation on samples), which we term the way weakly supervised.

A typical location extraction approach consists of two steps, location name detection and location entity disambiguation. Promising results have been obtained in the last decade based on natural language processing technologies. However, there are still two challenges which requires further investigation: 1)How to leverage the prior and contextual evidence to improve the location extraction performance, and 2) How to utilize the interdependence information between the named entity recognition step and disambiguation step. In this paper, we propose an iterative detection-ranking framework to address these problems as well as a set of novel features to mine contextual information from web resources. Experimental results show that our solution outperforms the state-of-the-art approaches, including Metacarta GeoTagger and Yahoo Placemaker.