Temples are an integral part of culture and heritage of India and are centers of religious practice for practicing Hindus. A scientific study of temples can reveal valuable insights into Indian culture and heritage. However to the best of our knowledge, learning resources that aid such a study are either not publicly available or non-existent. In this endeavour we present our initial efforts to create a corpus of Hindu temples in India. In this paper, we present a simple, re-usable platform that creates temple corpus from web text on temples. Curation is improved using classifiers trained on textual data in Wikipedia articles on Hindu temples. The training data is verified by human volunteers. The temple corpus consists of 4933 high accuracy facts about 573 temples. We make the corpus and the platform freely available. We also test the re-usability of the platform by creating a corpus of museums in India. We believe the temple corpus will aid scientific study of temples and the platform will aid in construction of similar corpuses. We believe both these will significantly contribute in promoting research on culture and heritage of a region.

We describe the work carried out by AMEX AI-LABS on an extractive summarization benchmark task focused on Financial Narratives Summarization (FNS). This task focuses on summarizing annual financial reports which poses two main challenges as compared to typical news document summarization tasks : i) annual reports are more lengthier (average length about 80 pages) as compared to typical news documents, and ii) annual reports are more loosely structured e.g. comprising of tables, charts, textual data and images, which makes it challenging to effectively summarize. To address this summarization task we investigate a range of unsupervised, supervised and ensemble based techniques. We find that ensemble based techniques perform relatively better as compared to using only the unsupervised and supervised based techniques. Our ensemble based model achieved the highest rank of 9 out of 31 systems submitted for the benchmark task based on Rouge-L evaluation metric.

Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in the KG directly affects an EL system’s ability to correctly link mentions in text to the entity in KG. This causes many EL systems to perform well for entities well connected to other entities in KG, bringing into focus the role of KG density in EL. In this paper, we propose Entity Linking using Densified Knowledge Graphs (ELDEN). ELDEN is an EL system which first densifies the KG with co-occurrence statistics from a large text corpus, and then uses the densified KG to train entity embeddings. Entity similarity measured using these trained entity embeddings result in improved EL. ELDEN outperforms state-of-the-art EL system on benchmark datasets. Due to such densification, ELDEN performs well for sparsely connected entities in the KG too. ELDEN’s approach is simple, yet effective. We have made ELDEN’s code and data publicly available.