ISO-TimeML is an international standard for multilingual event annotation, detection, categorization and linking. In this paper, we present the Hindi TimeBank, an ISO-TimeML annotated reference corpus for the detection and classification of events, states and time expressions, and the links between them. Based on contemporary developments in Hindi event recognition, we propose language independent and language-specific deviations from the ISO-TimeML guidelines, but preserve the schema. These deviations include the inclusion of annotator confidence, and an independent mechanism of identifying and annotating states such as copulars and existentials) With this paper, we present an open-source corpus, the Hindi TimeBank. The Hindi TimeBank is a 1,000 article dataset, with over 25,000 events, 3,500 states and 2,000 time expressions. We analyze the dataset in detail and provide a class-wise distribution of events, states and time expressions. Our guidelines and dataset are backed by high average inter-annotator agreement scores.

In this paper, we provide the basic guidelines towards the detection and linguistic analysis of events in Kannada. Kannada is a morphologically rich, resource poor Dravidian language spoken in southern India. As most information retrieval and extraction tasks are resource intensive, very little work has been done on Kannada NLP, with almost no efforts in discourse analysis and dataset creation for representing events or other semantic annotations in the text. In this paper, we linguistically analyze what constitutes an event in this language, the challenges faced with discourse level annotation and representation due to the rich derivational morphology of the language that allows free word order, numerous multi-word expressions, adverbial participle constructions and constraints on subject-verb relations. Therefore, this paper is one of the first attempts at a large scale discourse level annotation for Kannada, which can be used for semantic annotation and corpus development for other tasks in the language.

In this paper, we provide an alternate perspective on word representations, by reinterpreting the dimensions of the vector space of a word embedding as a collection of features. In this reinterpretation, every component of the word vector is normalized against all the word vectors in the vocabulary. This idea now allows us to view each vector as an n-tuple (akin to a fuzzy set), where n is the dimensionality of the word representation and each element represents the probability of the word possessing a feature. Indeed, this representation enables the use fuzzy set theoretic operations, such as union, intersection and difference. Unlike previous attempts, we show that this representation of words provides a notion of similarity which is inherently asymmetric and hence closer to human similarity judgements. We compare the performance of this representation with various benchmarks, and explore some of the unique properties including function word detection, detection of polysemous words, and some insight into the interpretability provided by set theoretic operations.