Abstract

Given the explosion in availability of scientific documents (books and research papers), automatically extracting cause–effect (CE) relation mentions, along with other arguments such as polarity, uncertainty and evidence, is becoming crucial for creating scientific knowledge bases from scientific text documents. Such knowledge bases can be used for multiple tasks, such as question answering, exploring research hypotheses and identifying opportunities for new research. Linguistically complex constructs are used to express CE relations in text, which requires complex natural language processing techniques for CE relation extraction. In this paper, we propose two machine learning techniques for automatically extracting CE relation mentions from documents in metallurgy and materials science domains. We show experimentally that our algorithms outperform several baselines for extracting intrasentence CE relation mentions. To the best of our knowledge, this is the first work for extraction of CE relations from documents in metallurgy and materials science domains.

Abstract

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in studying and countering sexism better. The existing work on sexism classification, which is different from sexism detection, has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization. We develop a neural solution for this multi-label classification that can combine sentence representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible, hierarchical architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. The best propose method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.

Abstract

INDIA IS A software superpower today. This achievement rests on more than four decades of work spanning software processes, rigorous engineering and value-adding technologies, among others. In this article, we present highlights of some of these activities. This regional section also contains other articles that complement this account of exciting work in software systems stemming from India.

Abstract

Prediction of trust relations between users of social networks is critical for finding credible information. Inferring trust is challenging, since userspecified trust relations are highly sparse and power-law distributed. In this paper, we explore utilizing community memberships for trust prediction in a principled manner. We also propose a novel method to model homophily that complements existing work. To the best of our knowledge, this is the first work that mathematically formulates an insight based on community memberships for unsupervised trust prediction. We propose and model the hypothesis that a user is more likely to develop a trust relation within the user’s community than outside it. Unlike existing work, our approach for encoding homophily directly links user-user similarities with the pair-wise trust model. We derive mathematical factors that model our hypothesis relating community memberships to trust relations and the homophily effect. Along with low-rank matrix factorization, they are combined into chTrust, the proposed multi-faceted optimization framework. Our experiments on the standard Ciao and Epinions datasets show that the proposed framework outperforms multiple unsupervised trust prediction baselines for all test user pairs as well as the low-degree segment, across evaluation setting

Abstract

The task of fact-checking has been formalised as the assessment of the truthfulness of a claim. Be it a political proclamation or a technological development, verification of a new tidbit of information before its propagation to the general public is of utmost importance. Failing to do so leads to the spread of misinformation, which is a devious tool. Fact-checking is commonly performed by journalists, manually looking up information pertaining to the statement in question. This is a drawn out and tedious process with a chance of the concerned person not covering the domain exhaustively. Some of this effort is reduced by the use of knowledge bases created over a period of time. In this work under Task 2 (Factuality) of the CLEF 2018 CheckThat! Lab, we detail a neural network based methodology which models the textual data of a claim based on various representations of its words and characters. An affixed attention mechanism allows us to encapsulate linguistic features common in false claims. We achieve an accuracy of 39.57% on the task dataset.

Abstract

An effective news recommendation system should harness the his- torical information of the user based on her interactions as well as the content of the articles. In this paper we propose a novel deep learning model for news recommendation which utilizes the content of the news articles as well as the sequence in which the articles were read by the user. To model both of these information, which are essentially of different types, we propose a simple yet effective architecture which utilizes a 3-dimensional Convolutional Neural Network which takes the word embeddings of the articles present in the user history as its input. Using such a method en- dows the model with the capability to automatically learn spatial (features of a particular article) as well as temporal features (fea- tures across articles read by a user) which signify the interest of the user. At test time, we use this in combination with a 2-dimensional Convolutional Neural Network for recommending articles to users. On a real-world dataset our method outperformed strong baselines which also model the news recommendation problem using neural networks.

Abstract

Science journalism is the art of conveying a detailed scientific research paper in a form that nonscientists can understand and appreciate while ensuring that its underlying information is conveyed accurately. It aims to transform jargon-laden scientific articles into a form that a common reader can comprehend while ensuring that the meaning of the article is retained. It plays a crucial role in making scientific content suitable for consumption by the public at large. Recent advances in Deep Learning research and it’s applications in natural language processing have made way to impressive results in Natural Language Generation. We leverage these advances to explore the possibility of their use in journalism, science journalism in particular, as comprehension of scientific content is much harder challenge than most of the other forms of content, like shorthand, which journalists use while writing articles. In this work, we introduce the problem of automated science journalism and present ways to automate some parts of the workflow by automatically generating the ‘title’ of a blog version of a scientific paper. We have built a corpus of 87, 328 pairs of research papers and their corresponding blogs from two science news aggregators and have used it to build ScienceBlogger - a pipeline-based architecture consisting of a two-stage mechanism to generate the blog titles. To demonstrate the models, we built an interactive tool, where a user can give abstract and title of a research paper, which would be processed by our APIs to produce a blog title, along with some relevant information about the model used for the generation. Evaluation using standard metrics indicate viability of the proposed system.

Abstract

With news stories coming from a variety of sources, it is crucial for news aggregators to present interesting articles to the user to maximize their engagement. This creates the need for a news recommendation system which understands the content of the articles as well as accounts for the users’ preferences. Methods such as Collaborative Filtering, which are well known for general recommendations, are not suitable for news because of the short life span of articles and because of the large number of articles published each day. Apart from this, such methods do not harness the information present in the sequence in which the articles are read by the user and hence are unable to account for the specific and generic interests of the user which may keep changing with time. In order to address these issues for news recommendation, we propose the Recurrent Attentive Recommendation Engine (RARE). RARE consists of two components and utilizes the distributed representations of news articles. The first component is used to model the user’s sequential behaviour of news reading in order to understand her general interests, i.e., to get a summary of her interests. The second component utilizes an article level attention mechanism to understand her specific interests. We feed the information obtained from both the components to a Siamese Network in order to make predictions which pertain to

Abstract

Domain Specific Search Engines are expected to provide relevant search results. Availability of enormous number of URLs across subdomains improves relevance of domain specific search engines. The current methods for seed URLs can be systematic ensuring representation of subdomains. We propose a fine grained approach for automatic extraction of seed URLs at subdomain level using Wikipedia and Twitter as repositories. A SeedRel metric and a Diversity Index for seed URL relevance are proposed to measure subdomain coverage. We implemented our approach for 'Security - Information and Cyber' domain and identified 34,007 Seed URLs and 400,726 URLs across subdomains. The measured Diversity index value of 2.10 conforms that all subdomains are represented, hence, a relevant 'Security Search Engine' can be built. Our approach also extracted more URLs (seed and child) as compared to existing approaches for URL extraction.

Abstract

Translation Quality Estimation (QE) aims to estimate the quality of an automated machine translation (MT) output without any human intervention or reference translation. With the increasing use of MT systems in various cross-lingual applications, the need and applicability of QE systems is increasing. We study existing approaches and propose multiple neural network approaches for sentence-level QE, with a focus on MT outputs in Indian languages. For this, we also introduce five new datasets for four language pairs: two for English–Gujarati, and one each for English–Hindi, English–Telugu and English–Bengali, which includes one manually post-edited dataset for English– Gujarati. These Indian languages are spoken by around 689M speakers world-wide. We compare results obtained using our proposed models with multiple state-ofthe-art systems including the winning system in the WMT17 shared task on QE and show that our proposed neural model which combines the discriminative power of carefully chosen features with Siamese Convolutional Neural Networks (CNNs) works best for all Indian language datasets.