Abstract

Sports data is probably the most widely looked at and analysed data source in the world. Data captured during a sports match is of much interest to fans who use data to validate their hypotheses about player/team performances and to conduct an analysis of the happenings during the game. Data collection has largely been a manual process and thus the data was discrete in nature and outcome based. For example in the case of cricket, for a ball bowled the outcome was recorded as to how many runs were scored but the position the ball was pitched and where it passed the batsman can not be recorded with a reasonable level of consistency, if done manually. With the introduction of various technology based tracking solutions, such kinds of spatial data that is consistent, can now be complied. When you think of continuous sports such as football, hockey this kind of data is continuous and holds much more context than that the discrete, outcome based data used to present. The 2 methods used currently are either tracking through hardware tracking devices that need to be attached to each object of interest or by using computer vision techniques to analyze videos of the game that must be recorded by multiple specially installed single purpose cameras. Hence, as the data made available is becoming richer we see that sports players and teams are increasingly making use of data to devise strategies and to gain a competitive advantage. This is making such data very powerful as sports teams can use it for prescribing tactics, betting companies for better predictions, and sport broadcasters and fans for analysis. Spatial data is especially interesting in football because the dynamic positions of the players on the playing field are highly important to decide the outcome of the game. Football players are often advised by their managers precisely as to how they must position themselves during different passages of play. Managers are often thought to be playing chess with each other as they plan their moves and organize their troops in various tactical formations aimed at giving them a leg-up over their opponent. The importance of spatial data in football cannot be overstated. We have taken a different approach of using computer vision to gather data pertaining to player and ball location by working purely off a broadcast video for the sport of football. The benefits of this approach are that it is economically cheaper and can also be used to analyze any televised match from the past. The 2 other techniques discussed are highly intrusive and expensive to setup. Another drawback with these techniques is that they need specially installed hardware prior to the start of the game to gather data. Using our technique we can analyze data of any televised football game easily and also use it to analyze any televised game for the past. As data hungry machine learning models are being increasingly used to prescribe and predict, opening up the data from all the games in the past could be hugely significant and we hope our research proves a significant step in that direction

Abstract

Replacing overexposed or dull skies in outdoor photographs is a desirable photo manipulation. It is often necessary to color correct the foreground after replacement to make it consistent with the new sky. Methods have been proposed to automate the process of sky replacement and color correction. However, many times a color correction is unwanted by the artist or may produce unrealistic results. Style similarity is an important measure for many applications such as style transfer, fashion search, art exploration, etc. However, computational modeling of style is a difficult task owing to its vague and subjective nature. Most methods for style based retrieval use supervised training with pre-defined categorization of images according to style. While this paradigm is suitable for applications where style categories are well-defined and curating large datasets according to such a categorization is feasible, in several other cases such a categorization is either ill-defined or does not exist. In this thesis, we primarily study various image representations and their applications in understanding visual style and automatic background replacement. First, we propose a data-driven approach to sky-replacement that avoids color correction by finding a diverse set of skies that are consistent in color and natural illumination with the query image foreground. Our database consists of ∼1200 natural images spanning many outdoor categories. Given a query image, we retrieve the most consistent images from the database according to L2 similarity in feature space and produce candidate composites. The candidates are re-ranked based on realism and diversity. We used pre-trained CNN features and a rich set of hand-crafted features that encode color statistics, structural layout, and natural illumination statistics, but observed color statistics to be the most effective for this task. We share our findings on feature selection and show qualitative results and a user-study based evaluation to show the effectiveness of the proposed method. Next, we propose an unsupervised protocol for learning a neural embedding of visual style of images. Our protocol for learning style based representations does not leverage categorical labels but a proxy measure for forming triplets of anchor, similar, and dissimilar images. Using these triplets, we learn a compact style embedding that is useful for style-based search and retrieval. The learned embeddings outperform other unsupervised representations for style-based image retrieval task on six datasets that capture different meanings of style. We also show that by fine-tuning the learned features with datasetspecific style labels, we obtain best results for image style recognition task on five of the six datasets. To the best of our knowledge, ours is the first work that provides a comprehensive review and evaluation of style representations in an unsupervised setting. Our findings along with the curated outdoor scene database would be useful to the community for future research in the direction of sky-search and sky-replacement

Abstract

Climate change calls for a unanimous global initiative, and all nations have been requested to reduce their growing greenhouse gas emissions to tackle this issue. In the United Nations’ 21st Conference on Climate Change, India has pledged to reduce its emission intensity by 33-35% of its 2005 level by 2030. Several studies predicted that coal-based electricity generation in India could double by 2040. At present, the residential building sector alone accounts for 24% of total electricity consumption in India, representing second-highest compounded annual growth rate of 7.58% after the Industrial sector. The Alliance for an Energy Efficient Economy (AEEE) in India predicted that the electricity consumption growth rate in residences would further increase in the future, with increasing residential built-stock and growth in ownership of cooling appliances. However, the growth rate in consumption may not necessarily be linear with increasing residential built-stock and cooling appliances ownership per unit household. Climate Change impact studies conducted on residential buildings in cold and tropical climates mostly infer that cooling energy consumption would rise with the predicted change in the climate. This study aims to assess the potential impact of climate change on Indian residential building energy consumption in five climatic zones, using a transient building energy simulation approach. Future typical weather files are generated for the year 2050 for these climatic zones, using Climate Change World Weather File Generator developed by University of Southampton. The present conventional (PC) and present energy-efficient (PE) building cases are modelled to identify their energy performance for present and future weather conditions. Further, the study includes cases with future energy conservation measures for PC building – Present Conventional Buildings with Future Energy-efficiency retrofit Measures (PCFM) and PE building – Present Energy-efficient Buildings with Future Energy-efficient retrofit Measures (PEFM) in the scope. The PCFM and PEFM cases are simulated only for future climate to estimate energy savings through future energy conservation measures. A future energy-efficient (FE) building case is also modelled and simulated with features involving high insulated envelope and future energy-efficient appliances. Various global studies on residential energy and the impact of climate change have focused on measures for new buildings, and on revision of insulation code. These studies focus on insulation levels for building envelope and retrofit measures for cooling system towards energy conservation. This work contributes by incorporating future retrofit technologies such as electrochromic windows with solar PV glass, and simple home automation strategies for coupling ceiling fans with air conditioning systems for future energy conservation. The variable loads like ceilings fans and geysers are modelled and simulated through Energy Management System scripting in EnergyPlus. The Building Integrated Photovoltaic (BIPV) system is also modelled and simulated as part of future energy conservation retrofit measures Based on energy simulations, it is observed that the present-day built residential buildings energy performance would be influenced by climate change. The global warming would increase the cooling energy consumption of present-day residential buildings leading to increase in total energy consumption. The consumption rate can be reduced to the levels less than the current energy consumption by deploying future energy efficient retrofit strategies for present-day buildings. The present-day energy efficient buildings can reduce their consumption by half in future whereas conventional buildings with low-thermal envelope and energy in-efficient appliances could reduce by more than half by retrofitting in the future. The facades of high-rise buildings are widely exposed to solar radiation leading to large solar ingress. However, the BIPV system deployment would not only reduce the heat ingress from the envelope but also convert some part of the solar radiation into electricity.

Abstract

In this work, we try to advance knowledge about differences in neural processing of music between musicians and non-musicians, both through our findings and methodologically. Music is omnipresent in our daily lives. Music listening has been found to have many positive benefits on health, and musicbased interventions have been shown to be an effective form of alternative and complimentary therapy to aid treatment for various disorders. Importantly, it has been suggested that an individual’s musical background and musicality play a key role in modulating the successful acquisition of the benefits music has to offer. Musical training has been shown to engender structural and functional changes in the brain due to intense repetitive sensory-motor demands. In addition to engendering enhanced sensitivity in the processing, representation, and discrimination of sounds and music, musical training has also been associated with transfer effects such as enhanced cognitive function in speech and language processing, motor abilities, attention and memory, etc. Music is thus an ideal tool to study brain adaptation and plasticity, and musicians an ideal group to study brain changes driven by experience, especially when juxtaposed with a non-musicians group. Thus, along with understanding the neural underpinnings of music feature processing, it is also essential to look at how musical training modulates the functionality of the brain by investigating group level differences between musicians and non-musicians. Our work is a step in that direction. A distinctive aspect of the work presented is the use of the naturalistic paradigm wherein participants listen to entire pieces of music without performing any task while being scanned and renders itself an attractive and more ecologically valid approach to studying music perception as opposed to the hitherto controlled paradigms. In our work, we look at the differences between musicians and non-musicians from two different viewpoints - from a segregated viewpoint (which aims to identify local regions that are specialized for a particular task) in our first study, and from an integrated viewpoint (which allows for identification and characterization of interactions between brain regions that allow for integrated functioning) in our second study. Group differences were identified in both the studies thereby revealing differences in neural encoding as a function of musical expertise. Musical training and expertise is likely to enable top-down analytic processing in the musicians, especially of the more complex and higher-level aspects and features of music, leading to differences in listening and processing strategies caused predominantly by their varied backgrounds. This resulted in lesser homogeneity at the group level for the musicians.On the other hand, the non-musicians exhibited greater within group consistencies, indicating primarily sensory, bottom-up processing. In our first study which takes a segregated approach, we investigate the differences between musicians and non-musicians in the encoding of acoustic features encompassing musical timbre, rhythm, and tonality in a continuous music listening paradigm. 18 musicians and 18 non-musicians were scanned using functional magnetic resonance imaging (fMRI) while listening to three 8-minute long musical pieces representing different musical styles. Acoustic features corresponding to timbre, rhythm, and tonality were computationally extracted from the stimuli and correlated with brain responses. Overall, non-musicians exhibited broader regions of correlations, implying greater within group similarities in musical feature processing, especially in the auditory and default mode network-related regions. Musicians demonstrated significant correlations in regions possessing adaptations to music processing due to training, in addition to greater involvement of limbic and reward regions in response to rhythm, indicative of greater affective and analytic processing. However, as a group, they did not exhibit large regions of consistent correlation patterns, especially in processing high-level features, which could be attributed to differences in processing strategies arising out of their varied training and background. For our second study which takes an integrated approach, we note that studies from a segregated viewpoint and studies involving network analysis have indicated the presence of large-scale brain networks involved in the processing of music and have highlighted the differences between musicians and non-musicians. However, network analysis studies of functional connectivity in the brain during music listening have thus far focused solely on static network analysis. Dynamic Functional Connectivity (DFC) studies have lately been found useful in uncovering meaningful, time-varying functional connectivity information in both resting-state and task-based experimental settings. We examin

Abstract

Current mobile technology does not utilize the spectrum efficiently. This problem is going to increase in future because of increase in wireless devices. Cognitive Radio (CR) is one of the solutions to exploit the underutilized spectrum. It is key enabler for next generation mobile technology 5G with applications such as efficient spectrum utilization and interference management. Spectrum sensing is an important component of cognitive radio networks (CRNs) as it provides spectrum awareness required for cognitive processing. In this thesis, we first investigate a distributed and heterogeneous CRN, comprising of secondary users (SUs) employing either energy detector (ED) or autocorrelation detector (AD) to detect the pres- ence or absence of an orthogonal frequency division multiplexing (OFDM) based primary user (PU). For the considered heterogeneous cooperative spectrum sensing (CSS), the optimal soft combining rule is derived. The performance of this optimal fusion rule and different hard combining schemes such as OR, AND and MAJORITY is presented for the case when the noise variance is exactly known. Later, the effect of noise uncertainty is also presented. The proposed heterogeneous CSS is shown to com- bine the excellent performance of the EDs (when the noise variance is exactly known) and robustness of the ADs to the noise uncertainty. However, the proposed soft combining based fusion rule assumes independence between the observations which may not hold in practical scenarios. Incorporating dependence between observations in fusion rule for CSS scheme forced us to look for alternative concepts and theories beyond standard concepts used in the context of spectrum sensing. Copula theory provides a new dimension to the picture. It enables us to incorporate dependence in the fusion process. Using the concept of copula theory, we forged a novel copula-based fusion rule for spectrum sensing. We investigate a distributed detection model where SUs employ ADs for the detection of a PU. In the presence of a PU, it is assumed that the observations across different SUs and subsequently the decision statistics are dependent. For the fusion of these dependent statistics, different copulas such as t-copula, Gaussian, Clayton and Gumbel are employed. In the presence of dependence among decision statistics, significant improvement in detection performance is observed while using copula theory instead of the traditional assumption of independence. Simulation results are presented to show the superiority of copula-based spectrum sensing.

Abstract

Object recognition deals with the recognition of objects belonging to various categories such as aeroplane, bird, chair etc. A more challenging sub-problem is fine-grained object recognition wherein objects belonging to the subordinate categories in a same entry level category are to be recognized; such as recognition of bird species and car models. It is one of the cornerstones in object recognition due to its potential in augmenting human vision. In the recent years, fine-grained object recognition using Image based Recognition (IR) has seen tremendous progress with the advent of convolutional neural networks. However, IR is cost-sensitive as it is excessively picture-dependent and fails without the adequate number of quality pictures. On the other hand, a picture-independent recognition approach called Question-answering based recognition (QR) has been investigated in which the visual semantic attributes of objects are exploited by posing them as questions to a human-in-the-loop. The responses received to the questions are then utilized for recognizing the test object. However, QR entails heavy human labor as it relies entirely on the human input for recognition. Subsequently, hybrid human-computer recognition called Image and Question answering based recognition (IQR) has been proposed which utilizes both IR and QR jointly for more effective recognition. Still, many of the works in IQR are picture-dependent making them cost-sensitive. The few picture-independent works under IQR which use binary/multiple choice question answering are slow and fail to produce the desirable performance as needed by the present recognition applications. To this end, in this thesis, we propose an improved batch-based question answering method for making the IQR recognition cost-effective, i.e., efficient and picture-independent. We refer to the proposed approach as Recognition via Image and Batch based local Question answering (RIBQ). Unlike the existing picture-independent recognition methods, the proposed method poses a batch of multiple discriminating questions at each time-step to the human-in-the-loop for distinguishing the probable classes. As a part of this, we introduce a cluster based local feature extraction method for finding the best set of N highly discriminating questions to pose to the human-in-the-loop at each time-step. At each time-step, the proposed method first clusters the probable classes into N clusters. From each of the obtained clusters, we mine an attribute whose expected presence/absence is exclusively predominant in that cluster using the proposed concept of locality degree of an attribute. The mined set of attributes from the clusters called as cluster-centric local attributes are then posed as questions to the human-in-the-loop. Based on the responses received, the class probability estimates are re-computed and the probable classes to be considered in the next time-step are determined. After a preset number of time-steps, the most probable class of the target object is returned as the final prediction. In this way, we make the recognition process much faster and robust by modelling localized information obtained from the responses at each time-step. When pictures are available, for more qualitative recognition, we make the human-in-the-loop and vision supplement each other by facilitating the plugin of vision-based predictions into the framework. Experiments on three challenging datasets show significant performance improvement with respect to accuracy and computation time as compared to the existing schemes. The results demonstrate that the proposed concept of batch-based local question answering identifies potential discriminating questions in an efficient manner. They in-turn facilitate processing and knowledge acquisition at a faster rate as compared to the questions posed in the existing question answering approaches, leading to better recognition. Hence, the proposed method achieves effective recognition regardless of the availability of pictures, making it cost-effective. Overall, the proposed approach provides scope for developing efficient information extraction methodologies in all human-in-the-loop based recognition systems.

Abstract

Social media sites such as Twitter and Facebook have become people’s main channels for connecting and voicing their opinions with other individuals and the community. With the growth of social media platforms in India and people expressing themselves in multiple languages and the open and casual nature of these sites leads to more people voicing themselves in their native language, resulting in a greater amount of code-mixed content that is currently lacking in the annotated set of data. Through the access to public opinion on almost every subject, we can collect a huge amount of user data that could prove useful to different businesses, making tasks such as opinion mining, sentiment analysis and opinion mining even more essential. Therefore, recently the task of understanding users in low-resource languages has become the most researched task. With the penetration of the Internet and its proliferation into multilingual societies, the linguistic diversity of the real world is now reflected in online communities. Limiting solutions to a set of major languages is no longer viable. Nevertheless, the proliferation is relatively recent, and hence the amount of available data in many widely-spoken languages is inadequate. Manual annotation of data is a hu- mongous task and often expensive. In this thesis, we aim towards working on these widely-spoken languages to understand users better when they express themselves in their native language on social media platforms. Understanding people broadly means understanding their feeling, opinion, stance towards a particular target, and thus it brings in the tasks of stance and humor detection. We are mainly focusing on text classification which refers to the process of determining text categories based on the text content under a given classification system. We present new solutions for stance detection and the other for detection of humor in English-Hindi code-mixed tweets. The tweets for stance detection are collected for the target topic - ‘Demonetisation.’ Deep neural network models have been widely used in the field of natural language processing (NLP). Convolutional Neural Network’s (CNN’s) and Recurrent neural networks (RNN’s), which are able to process sequences of variable length, are known methods for sequence modeling tasks. Long-short-term memory (LSTM) is one category of RNNs and has achieved exceptional text classification efficiency. Nonetheless, due to the high dimensionality and sparse text data and the dynamic semantics of the natu- ral language, text classification poses demanding challenges — a novel and integrated architecture that incorporates a bidirectional LSTM (BiLSTM) to solve the above problems. A novel and unified archi- tecture containing a bidirectional LSTM (BiLSTM), attention mechanism, and a convolutional layer are proposed in this paper in order to solve the above problems.

Abstract

Ontology, as a philosophical discipline of the study of reality, has gained traction in recent times and is appropriated into computer science giving rise to a new discipline called ‘Ontology Engineering’. One of the main goals of ontology engineering, is how to specify meaning to a computer. But that question takes us back to the foundational issue of what meaning is, and how does one go about studying it so that one can specify it to a computer as explicitly and as accurately as possible. The answer to this question lies in ‘Formal Ontology’. Formal ontology is the study of forms of entities in reality as opposed to their contents. The term was first coined by Edmund Husserl who envisioned it as the ‘eidetic science of the object as such’ i.e., the study of all those features of an object insofar as they make it what it is i.e., an object. But what the forms of entities in reality are, and how to study them is still anopen issue. The notion of a formal ontology so far has been mainly understood as the application offormal logic, particularly First-Order Logic (FOL) and its variants, to ontology. But since FOL is based on propositional form which is primarily a syntactic form, and does not have any semantics of its own, it requires something outside itself to give semantics to it. One needs to look at various conceptions of reality to provide such semantics. We look at three such conceptions of reality: (1) as sets of entities (2) as parts and wholes, and (3) as states of affairs (SOAs), and claim that the SOA-conception of reality is the most apt candidate to study reality formally and, in turn, to do formal ontology. But we also show that the propositional form, and in turn, FOL, is incapable of capturing the structure of SOAs. Though there had been various attempts to overcome the problems of classical logic like FOL by construction of new and alternative logics, almost all these logics still swear by the fundamental notion of the subject-predicate form ( propositional form ) which is fundamentally a sentential or linguistic or logical form as opposed to an entitative or extra-linguistic or ontological form. Hence these logics are also incapable of capturing the structure of SOAs. In fact, there has been so far no proper ontological form which captures the structure of SOAs, in toto . We propose a non-propositional, non-set-theoretic, onto-logical form , called punctuator to capture the structure of SOAs in toto , and in turn to do formal ontology. We then consider Vaiśeṣika, the foundational ontology in Indian analytic tradition, and present a formalized version of it - Neo-Vaiśeṣika Formal Ontology (NVFO) - using only punctuators to give an instance of a formal ontology as construed above. NVFO was founded by Singh (2002, 2003, 2008) and later refined and extended by Navjyoti Singh and Rajesh Tavva (Tavva & Singh, 2015). Notwithstanding our appeal to take recourse to formal ontology to specify meaning to a computer explicitly, we also propose a novel idea called generative ontology to make the specification of this meaning to a computer as accurate as possible. In a generative ontology, a potentially infinite number of complex ontological structures (SOAs/punctuators) can be generated from a finite number of atomic (or irreducible) ontological structures (SOAs/punctuators). This becomes possible because the form of punctuator is recursive in nature. A punctuator is a boundary (a non-entity) which brings two entities together in some relational context R which, itself, is further composed of chains of entities (and punctuators between them), hence making the structure of a punctuator complex as well as recursive. Because of this complexity of a punctuator as a web of entities and punctuators between them, the structure of a punctuator can be best understood as a graph (as composed of nodes and edges). Graph grammars, being a natural extension of string as well as tree grammars, are highly expressive and powerful enough to capture the generative structure of a wide variety of scenarios - both simple and complex. Unfortunately, this power is not leveraged by many. Most of the graph grammars available hitherto are either toy grammars or limited to addressing highly specialized problems. The current thesis presents a domain-independent grammar i.e., a grammar for reality, as theorized in Vaiśeṣika. We contend that Vaiśeṣika ontology, given that it implicitly exhibits the ontological structure of a punctuator, is one such ontology which can be generated from some basic alphabet of punctuators. Hence it is not only a formal ontology but also a generative ontology. Given the fact that most of the existing foundational ontologies (those which deal not with a particular domain of reality but with entire reality as such) are not generated but manually constructed, the idea of a generative ontology becomes significant because of its capacity to impar

Abstract

The domain of cultural mapping deals with capturing and representing knowledge regarding diverse tangible (physical artifacts, architectural landmarks, etc.) and intangible aspects of cultural heritage (language, performing arts, social practices, rituals, festive events etc.) which involve hybrid data sets from incompatible sources with different terminologies and representational schemas. Access to desired information is constrained by different terminologies for same concepts and difficulties in querying across data sets. Documentation and representation of knowledge in the domains of tangible and intangible cultural heritage along with the need for preservation of perishable data emphasize the need for specialized cataloging metadata. Semantic web can be used to ease interoperability and allow information exchange among different agents dealing with cultural aspects of a region or a community. Furthermore, the complex schema integration problem in this domain can be addressed efficiently by use of formal ontologies which provide universal schema to represent data from heterogeneous sources. One such widely accepted formal ontology to facilitate the integration, mediation, interchange of heterogeneous cultural heritage Information is CIDOC-CRM. We propose and implement an extension of CIDOC CRM ontology for the purpose of cultural mapping of villages of India. With help of four different case studies, we discuss how the framework provides an approach by which diverse cultural network semantics operating at the granularity of villages in India can be described independently of cultural data sources and in turn provide a schema allowing cultural data exchange among information systems and users. As part of one of the case studies we employ the model for construction of an open, interconnected semantic knowledge graph capturing and representing the cultural information for all the villages of India and at the same time preserving the unique cultural reality of each individual village. Building upon the Census of India data mapped to the ontological extension, we further implement different pipelines with help of custom built tools and applications to import information from subject matter experts (experts on local culture of the village), surveys and descriptive textual data to populate the semantic knowledge graph. The knowledge representation of intangible cultural heritage is highlighted through a case study and another use case of the model described is representation of the history of a village in terms of a graph of events influencing each other. The thesis also provides a critical analysis of domain of cultural mapping highlighting the generic intellectual and pragmatic challenges faced while representing cultural knowledge.

Abstract

Various philosophers including Bertrand Russell and Ludwig Wittgenstein have shared the view that extra-linguistic reality and its nature are somehow linked with language. Given that language is a medium of capturing the objective truth around us, it is possible that the formal logic of language could also be inspired by how the world around us operates. Reality as we understand it, is a series of continuous interactions between objects. These interactions are loosely referred to us by as events. With constant rise in content generated every day, it has become arduous task to keep track of all the events happening in the real world. A major challenge information access tasks face is that they have to deal with colossal amounts of unstructured data, i.e., data whose format loosely implies its meaning described in a human understandable language. This makes understanding event mentions in text a key problem in Natural Language Processing. Event detection is an important Natural Language Processing and Information Retrieval problem, that has been extended to multiple domains and is now being tackled for languages other than English.In this thesis, we address the problem of event detection in Hindi. Hindi, like many other languages is an object-centric language that grounds a metaphysical system in objects, with sentence constructions mainly describing the properties (state) of a noun or the interaction between these nouns. If we were to use Hindi to define the nature of being, real world events would be formally captured as a series of interactions between nouns. We are further motivated by the fact that, in the past few years, the doctrine that actions are events has become an essential, and sometimes an unargued part of the received view in the philosophy of action, despite the efforts of a few philosophers to undermine the consensus. This task is an important one given the constant rise in volume of Hindi data being produced and consumed. A recent report shows that Hindi content consumption on the web is growing at a rate of 94% per year as compared to English’s 19%1 . Given a low resource language like Hindi, with abundance in unstructured text, we believe leveraging systems to automatically detect events in unstructured texts can enable significant advances in the performance of various information access tasks. The task of event detection in Hindi is relatively unexplored, partly because of syntactic features such as relative free word order and nature of verb adjuncts and partly due to the lack of structured data in Hindi. These properties of Hindi, increase the number of options available for expressing structured information, hence significantly complicating how chunks of text can be semantically interpreted.From the perspective of extracting latent information, defining events as semantico-syntactic objects allows for extracting relations between both events and entities, that are not explicitly mentioned. The extracted event information can then be readily consumed by downstream tasks such as Question Answering, Summarization and Knowledge Graph generation. The initial work presented in this thesis is directed towards tackling two major challenges. The primary being understanding how event mentions are captured in Hindi. This task, motivated by the interesting property of Hindi, where the syntax of the sentence is influenced by the event-semantics, becomes a compelling one. The observations of our studies are captured and formulated as two documents (annotation guidelines and annotation specifications) that allow accurate identification of event mentions in Hindi text. The second challenge we tackle is that of automating the task of event mention detection in Hindi texts. In order to kickstart exploration in the domain of machine learning approaches, we build and publish the largest event annotated dataset for Hindi. Our research sheds lights on the various possible methods to automate event detection in Hindi texts and compares the impact of techniques built on top of and without hand-crafted features. Machine learning techniques for automated event-detection have advanced fairly owing to the current rise in introduction and adoption of sophisticated neural architectures. However, these architectures are plagued by biases in opportunistically collected data sets for major languages. As a result of this, there is little to no progress in development of resources and solutions for low resource languages. Driven by this motivation, the final part of work attempts to tackle this by introducing an architecture, the MultiLingual Sequence Tagger (M-LiST) capable of training on a combination of four monolingual datasets and attaining state of the art performance for open domain event detection on the in three languages and current best performance for one of the languages.