Abstract

Utility mining has been emerging as an important area in data mining. While existing works on utility mining have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this pa-per considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three-fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be filled. Second, we propose a novel flexible and efficient index, designated as the STUI index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots. Third, we conducted an extensive performance evaluation using real datasets to demonstrate the overall effectiveness of the proposed indexing scheme in terms of execution time and utility (net revenue) as compared to a recent existing scheme.

Abstract

Computational Core for Plant Metabolomics (CCPM) is a web-based collaborative platform for researchers in the field of metabolomics to store, analyze and share their data. Metabolomics is a newly emerging field of ‘omics’ research concerned with the characterization of large numbers of metabolites using chromatography in conjunction with mass spectrometry and NMR. There is abundant volume and variety in the data, and unpredictable velocity. An interdisciplinary engagement such as this faces significant non-technical challenges solvable using a balanced approach to software management in a university setting to create an environment promoting collaborative contributions. In this paper we report on our experiences, challenges and methods in delivering a usable solution. CCPM provides a secure data repository with advanced toolsfor analysis including preprocessing, pretreatment, data filtration, statistical analysis, and pathway analysis functions; and also visualization,integration and sharing of data. As all users are not equally IT-savvy,it is essential that the user interface is robust, friendly and interactive where the user can submit and control various tasks running simultane-ously without stopping/interfering with other tasks. In each stage of its pipeline architecture, users are also allowed to upload external data that has been partially processed till the previous stage in other platforms.Use of open source softwares for development makes the maintenance and development of our modules easier than others which depend on proprietary softwares.

Abstract

Sampark is machine translation system providing translations among nine pairs of Indian languages. Machine translation system is a class of natural language processing applications that is far more complex and highly compute intensive in nature. As the load on the deployed system increases, optimization becomes a challenge. Caching is one of the available options to improve the performance of a software system with increasing load which exhibit the characteristics of locality of reference. Sampark MT system being a natural language processing application exhibits this characteristic.Memcachedis a well-known, simple, in-memory caching solution that has been applied to improve the performance of several distributed web applications in the past. This paper describes how memcached has been applied to improve the performance of Sampark machine translation service which is deployed on a large cluster of machines. By applying distributed caching to MT system the performance of the system has improved upto 40%

Abstract

This paper investigates the application of unsupervised acoustic unit discovery for topic identification (topic ID) of spoken audio documents. The acoustic unit discovery method is based on a non-parametric Bayesian phone-loop model that segments a speech utterance into phone-like categories. The discovered phone-like (acoustic) units are further fed into the conventional topic ID framework. Using multilingual bottleneck features for the acoustic unit discovery, we show that the proposed method outperforms other systems that are based on cross-lingual phoneme recognizer.

Abstract

We investigate the problem of parsing conversational data of morphologically-rich languages such as Hindi where argument scrambling occurs frequently. We evaluate a state-of-the-art non-linear transition-based parsing system on a new dataset containing 506 dependency trees for sentences from Bollywood (Hindi) movie scripts and Twitter posts of Hindi monolingual speakers. We show that a dependency parser trained on a newswire treebank is strongly biased towards the canonical structures and degrades when applied to conversational data. Inspired by Transformational Generative Grammar (Chomsky, 1965), we mitigate the sampling bias by generating all theoretically possible alternative word orders of a clause from the existing (kernel) structures in the treebank. Training our parser on canonical and transformed structures improves performance on conversational data by around 9% LAS over the baseline newswire parser.

Abstract

In this paper, we report our participation in the Task 2: Triple Scoring of WSDM Cup challenge 2017. In this task, we were provided with triples of "type-like" relations which were given human-annotated relevance scores ranging from 0 to 7, with 7 being the "most relevant" and 0 being the "least relevant". The task focuses on two such relations: profession and nationality. We built a system which could automatically predict the relevance scores for unseen triples. Our model is primarily a supervised machine learning based one in which we use well-designed features which are used to a make a Logistic Ordinal Regression based classification model. The proposed system achieves an overall accuracy score of 0.73 and Kendall’s tau score of 0.36.

Abstract

This paper presents the ongoing process in the design of the first phase of the phonetically balanced codemixed corpus of Hindi-English speech (PBCM-Phase I). The reference corpus is a large code-mixed (LCM) newspaper corpus selected from the sections that contain frequent English insertions in a matrix of Hindi sentence. From a phonetically transcribed corpus, compulsory inclusion of lowest frequency triphones has been ensured, with the assumption that high frequency phones may automatically be included. A high correlation of 0.81 with the representative large corpus has been observed. A small scale speech corpus of 5.6 hours has been collected, by the contribution of 4 volunteer native Hindi speakers. The recording has been conducted in a professional recording studio environment. As a second contribution, this paper also presents a baseline recognition system with pooled monolingual and codemixed speech datasets as training and testing environments.

Abstract

Trivia is any fact about an entity which is interesting due to its unusualness, uniqueness or unexpectedness. Trivia could be successfully employed to promote user engagement in various product experiences featuring the given entity. A Knowledge Graph (KG) is a semantic network which encodes various facts about entities and their relationships. In this paper, we propose a novel approach called DBpedia Trivia Miner (DTM) to automatically mine trivia for entities of a given domain in KGs. The essence of DTM lies in learning an Interestingness Model (IM), for a given domain, from human annotated training data provided in the form of interesting facts from the KG. The IM thus learnt is applied to extract trivia for other entities of the same domain in the KG. We propose two different approaches for learning the IM - a) A Convolutional Neural Network (CNN) based approach and b) Fusion Based CNN (F-CNN) approach which combines both hand-crafted and CNN features. Experiments across two different domains - Bollywood Actors and Music Artists reveal that CNN automatically learns features which are relevant to the task and shows competitive performance relative to hand-crafted feature based baselines whereas FCNN significantly improves the performance over the baseline approaches which use hand-crafted features alone. Overall, DTM achieves an F1 score of 0.81 and 0.65 in Bollywood Actors and Music Artists domains respectively.

Abstract

The paper presents an automatic speech recognition (ASR) system for code-mixed read speech in Hindi-English, developed upon the extrapolation of monolingual training resources. A monolingual Hindi acoustic model, mixed with code-mixed speech data has been implemented to train a neural network based speech recognition framework. The testing corpus also follows a similar structure: containing data from both monolingual and code-mixed speech. The shared phonetic transcription, captured in WX notation has been exploited to harness the commonality between the pooled phonesets of Hindi and English. The experiments have been conducted in two separate formulations of a trigram based language model 1) In the first experiment, the language model contains no out-of-vocabulary words, as the test utterances are included in the training of the language model. The word error rate in this case has been obtained to be 10.63 %. 2) In the second experiment, the testing utterances have been excluded from the training language model. The word error rate in this case has been obtained to be 41.66 %.

Abstract

Code-Mixing (CM) is a natural phenomenon observed in many multilingual societies and is becoming the preferred medium of expression and communication in online and social media fora. In spite of this, current Question Answering (QA) systems do not support CM and are only designed to work with a single interaction language. This assumption makes it inconvenient for multi-lingual users to interact naturally with the QA system especially in scenarios where they do not know the right word in the target language. In this paper, we present WebShodh - an end-end web-based Factoid QA system for CM languages. We demonstrate our system with two CM language pairs: Hinglish (Matrix language: Hindi, Embedded language: English) and Tenglish (Matrix language: Telugu, Embedded language: English). Lack of language resources such as annotated corpora, POS taggers or parsers for CM languages poses a huge challenge for automated processing and analysis. In view of this resource scarcity, we only assume the existence of bi-lingual dictionaries from the matrix languages to English and use it for lexically translating the question into English. Later, we use this loosely translated question for our downstream analysis such as Answer Type(AType) prediction, answer retrieval and ranking. Evaluation of our system reveals that we achieve an MRR of 0.37 and 0.32 for Hinglish and Tenglish respectively. We hosted this system online and plan to leverage it for collecting more CM questions and answers data for further improvement.