Rahul Jha

Predicting the impact of scientific concepts using full-text features

New scientific concepts, interpreted broadly, are continuously introduced in the literature, but relatively few concepts have a long‐term impact on society. The identification of such concepts is a challenging prediction task that would help multiple parties—including researchers and the general public—focus their attention within the vast scientific literature. In this paper we present a system that predicts the future impact of a scientific concept, represented as a technical term, based on the information available from recently published research articles. We analyze the usefulness of rich features derived from the full text of the articles through a variety of approaches, including rhetorical sentence analysis, information extraction, and time‐series analysis. The results from two large‐scale experiments with 3.8 million full‐text articles and 48 million metadata records support the conclusion that full‐text features are significantly more useful for prediction than metadata‐only features and that the most accurate predictions result from combining the metadata and full‐text features. Surprisingly, these results hold even when the metadata features are available for a much larger number of documents than are available for the full‐text features.

NLP-driven citation analysis for scientometrics

This paper summarizes ongoing research in Natural-Language-Processing-driven citation analysis and describes experiments and motivating examples of how this work can be used to enhance traditional scientometrics analysis that is based on simply treating citations as a ‘vote’ from the citing paper to cited paper. In particular, we describe our dataset for citation polarity and citation purpose, present experimental results on the automatic detection of these indicators, and demonstrate the use of such annotations for studying research dynamics and scientific summarization. We also look at two complementary problems that show up in Natural-Language-Processing-driven citation analysis for a specific target paper. The first problem is extracting citation context, the implicit citation sentences that do not contain explicit anchors to the target paper. The second problem is extracting reference scope, the target relevant segment of a complicated citing sentence that cites multiple papers. We show how these tasks can be helpful in improving sentiment analysis and citation-based summarization.

Content Models for Survey Generation: A Factoid-Based Evaluation

We present a new factoid-annotated dataset for evaluating content models for scientific survey article generation containing 3,425 sentences from 7 topics in natural language processing. We also introduce a novel HITS-based content model for automated survey article generation called HITSUM that exploits the lexical network structure between sentences from citing and cited papers. Using the factoid-annotated data, we conduct a pyramid evaluation and compare HITSUM with two previous state-of-the-art content models: C-Lexrank, a network based content model, and TOPICSUM, a Bayesian content model. Our experiments show that our new content model captures useful survey-worthy information and outperforms C-Lexrank by 4% and TOPICSUM by 7% in pyramid evaluation.

Where the Photos Were Taken: Location Prediction by Learning from Flickr Photos

In this chapter, we explore the characteristics of geographically tagged Internet photos and determine their location based on the visual content. We develop a principled machine learning model to estimate geographical locations of photos by modeling the relationship between location and the photo content. To build reliable geographical estimators, it is important to find distinguishable geographical clusters in the world. These clusters cover general geographical regions not limited to just landmarks. Geographical clusters provide more training samples and hence lead to better recognition accuracy. We develop a framework for geographical cluster estimation, and employ latent variables to estimate the geographical clusters. To solve this estimation problem, we propose to build an efficient solver to find the latent clusters. We illustrate detailed qualitative results obtained from beaches photos taken at different continents. In addition, we show significantly improved quantitative results over other approaches for recognizing different beaches using the Flickr beach dataset as validation.