Abstract

We propose an unsupervised approach to extract all competing algorithms present in a given scholarly article. The algorithm names are treated as named entities and natural language processing techniques are used to extract them.All extracted entity names are linked with their respective original papers in the reference section by our novel entity-citation linking algorithm. Then the seen citation pairs are ranked based on the number of comparison related cue-words present in the entity-citation context. We manually annotated a small subset of DBLP Computer Science conference papers and report both qualitative and quantitative results of our algorithm on it

Abstract

With the advent of statistical modeling in sports, predicting the outcome of a game has been established as a fundamental problem.Cricket is one of the most popular team games in the world. With this article, we embark on predicting the outcome of a One Day International (ODI) cricket match using a supervised learning approach from a team composition perspective. Our work suggests that the relative team strength between the competing teams forms a distinctive feature for predicting the winner. Modeling the team strength boils down to modeling individual player’s batting and bowling performances, forming the basis of our approach. We use career statistics as well as the recent performances of a player to model him. Player independent factors have also been considered in order to predict the outcome of a match. We show that the k-Nearest Neighbor (kNN) algorithm yields better results as compared to other classifiers

Abstract

In this paper we investigate the problem of user authentication based on keystroke timing pattern. We propose a simple, robust and non parameterized nearest neighbor regression based feature ranking algorithm for anomaly detection. Our approach successfully handle drawbacks like outlier detection, scale variation and prevents over fitting.Apart from using existing keystroke timing features from the dataset liked well time and flight time, other features namely big ram time and inversion ratio time are engineered as well. The efficiency and effectiveness ofour method is demonstrated through extensive comparisons with others tate-of-the-art techniques using CMU keystroke dynamics bench markage equal error rate(EER) than other proposed techniques. We achieved an average equal error rate of0.051for the user authentication task.

Abstract

Shallow discourse parsing enables us to study discourse as a coherent piece of information rather than a sequence of clauses, sentences and paragraphs. In this paper, we identify arguments of explicit discourse relations in Hindi. This is the first such work carried out for Hindi. Building upon previous work carried out on discourse connective identification in Hindi, we propose a hybrid pipeline which makes use of both sub-tree extraction and linear tagging approaches. We report state-ofthe-art performance for this task.

Abstract

The current web APIs are end-user centric as they mostly focus on the end results. In this paper, we break this paradigm for one class of scientific workflow problems—machine translation, by designing an API that caters not only to the end users but also allows researchers to find bugs in their systems by exposing the ability to programmatically manipulate the results. Moreover, it follows an easy to replicate workflow based mechanism, which is built on the concept of microservices

Abstract

In Computational Linguistics, Hindi and Urdu are not viewed as a monolithic entity and have received separate attention with respect to their text processing. From part-of-speech tagging to machine translation, models are separately trained for both Hindi and Urdu despite the fact that they represent the same language. The reasons mainly are their divergent literary vocabularies and separate orthographies, and probably also their political status and the social perception that they are two separate languages. In this article, we propose a simple but efficient approach to bridge the lexical and orthographic differences between Hindi and Urdu texts. With respect to text processing, addressing the differences between the Hindi and Urdu texts would be beneficial in the following ways: (a) instead of training separate models, their individual resources can be augmented to train single, unified models for better generalization, and (b) their individual text processing applications can be used interchangeably under varied resource conditions. To remove the script barrier, we learn accurate statistical transliteration models which use sentence-level decoding to resolve word ambiguity. Similarly, we learn cross-register word embeddings from the harmonized Hindi and Urdu corpora to nullify their lexical divergences. As a proof of the concept, we evaluate our approach on the Hindi and Urdu dependency parsing under two scenarios: (a) resource sharing, and (b) resource augmentation. We demonstrate that a neural network-based dependency parser trained on augmented, harmonized Hindi and Urdu resources performs significantly better than the parsing models trained separately on the individual resources. We also show that we can achieve near state-of-the-art results when the parsers are used interchangeably.

Abstract

We present a comparative error analysis of two parsers - MALT and MST on Telugu Dependency Treebank data. MALT and MST are currently two of the most dominant data-driven dependency parsers. We discuss the performances of both the parsers in relation to Telugu language. we also talk in detail about both the algorithmic issues of the parsers as well as the language specific constraints of Telugu.The purpose is, to better understand how to help the parsers deal with complex structures, make sense of implicit language specific cues and build a more informed Treebank.

Abstract

Syntactic parsing in NLP is the task of working out the grammatical structure of sentences. Some of the purely formal approaches to parsing such as phrase structure grammar, dependency grammar have been successfully employed for a variety of languages. While phrase structure based constituent analysis is possible for fixed order languages such as English, dependency analysis between the grammatical units have been suitable for many free word order languages. These approaches rely on identifying the linguistic units based on their formal syntactic properties and establishing the relationships between such units in the form of a tree. Instead, we characterize every morphosyntactic unit as a mapping between form and function on the lines of Construction Grammar and parsing as identification of dependency relations between such conceptual units. Our approach to parser annotation shows an average MALT LAS score of 82.21% on Tamil gold annotated corpus of 935 sentences in a five-fold validation experiment.

Abstract

The Linguistic Annotation Workshop (LAW) is organized annually by the Association for Computational Linguistics’ Special Interest Group for Annotation (ACL SIGANN). It provides a forum to facilitate the exchange and propagation of research results concerned with the annotation, manipulation, and exploitation of corpora; work towards harmonisation and interoperability from the perspective of the increasingly large number of tools and frameworks for annotated language resources; and work towards a consensus on all issues crucial to the advancement of the field of corpus annotation. The series is now in its tenth year, with these proceedings including papers that were presented at LAW X, held in conjunction with the annual meeting of the Association for Computational Linguistics (ACL) in Berlin, Germany, on August 11, 2016.

Abstract

This paper evaluates the challenges involved in shallow parsing of Dravidian languages which are highly agglutinative and morphologically rich. Text processing tasks in these languages are not trivial because multiple words concatenate to form a single string with morpho-phonemic changes at the point of concatenation. This phenomenon known as Sandhi, in turn complicates the individual word identification. Shallow parsing is the task of identification of correlated group of words given a raw sentence. The current work is an attempt to study the effect of Sandhi in building shallow parsers for Dravidian languages by evaluating its effect on Malayalam, one of the main languages from Dravidian family. We provide an in-depth analysis of effect of Sandhi in developing a robust shallow parser pipeline with experimental results emphasizing on how sensitive the individual components of shallow parser are, towards the accuracy of a sandhi splitter. Our work can serve as a guiding light for building robust text processing systems in Dravidian languages.