Abstract

Pregroup calculus has been used for the representation of free word order languages (Sanskrit and Hungarian), using a construction called precyclicity. However, restricted word order alternation has not been handled before. This paper aims at introducing and formally expressing three methods of representing word order alternation in the pregroup representation of any language. This paper describes the word order alternation patterns of Hindi, and creates a basic pregroup representation for the language. In doing so, the shortcoming of correct reductions for ungrammatical sentences due to the current apparatus is highlighted, and the aforementioned methods are invoked for a grammatically accurate representation of restricted word order alternation. The replicability of these methods is explained in the representation of adverbs and prepositional phrases in English.

Abstract

Sentiment Analysis and other semantic tasks are commonly used for social media textual analysis to gauge public opinion and make sense from the noise on social media. The language used on social media not only commonly diverges from the formal language, but is compounded by codemixing between languages, especially in large multilingual societies like India. Traditional methods for learning semantic NLP tasks have long relied on end to end task specific training, requiring expensive data creation process, even more so for deep learning methods. This challenge is even more severe for resource scarce texts like codemixed language pairs, with lack of well learnt representations as model priors, and task specific datasets can be few and small in quantities to efficiently exploit recent deep learning approaches. To address above challenges, we introduce curriculum learning strategies for semantic tasks in code-mixed Hindi-English (Hi-En) texts, and investigate various training strategies for enhancing model performance. Our method outperforms the state of the art methods for Hi-En codemixed sentiment analysis by 3.31% accuracy, and also shows better model robustness in terms of convergence, and variance in test performance.

Abstract

The aim of the current study is to propose acoustic features for detection of nasals in continuous speech. Acoustic features that represent certain characteristics of speech production are extracted. Features representing excitation source characteristcs are extracted using zero frequency filtering method. Features representing vocal tract system characteristics are extracted using zero time windowing method. Feature sets are formed by combining certain subsets of the features mentioned above. These feature sets are evaluated for their representativeness of nasals in continuous speech in three different languages, namely, English, Hindi and Telugu. Results show that nasal detection is reliable and consistent across all the languages mentioned above.

Abstract

For voiced speech, significant excitation of the vocal tract system is due to sequence of impulse-like events in the vocal fold vibration. Significant excitations take place around the instants of glottal closure (GCIs), which are often referred as epochs. In literature, most of the epoch extraction algorithms rely on estimation of the excitation source, by attempting to remove the vocal tract system characteristics. Performance of these algorithms depends on the effectiveness of excitation source estimation methods. Algorithms such as zero frequency filtering (ZFF) and speech event detection using the residual excitation and a mean-based signal (SEDREAMS) detect epochs from the smoothened speech signal by exploiting temporal cues. But these algorithms rely on the value of pitch period. The objective of this paper is to develop an algorithm which does not require any apriori information of pitch period, and can be …

Abstract

The most prevalent multilingual Text-to-Speech (TTS) synthesis systems encounter an unnatural speaker shift at the language boundaries. This is observed when they are employed for code-mixed TTS synthesis. For the very fact that the collection of polyglot speech is non-trivial, many alternative approaches have been in focus. Cross-Lingual Voice Conversion (CLVC) has been one of those to generate speech with desired speaker and language identities. Our aim in this paper is to design a light-weighted CLVC framework between a pair of Mandarin-English speakers. CLVC is challenging when compared to traditional Voice Conversion (VC) because of its nature of accommodating unaligned corpus from the source and target speakers. We thus focus on generating a parallel corpus for CLVC and bridging the gap between speakers and languages. We perform a text-independent voice conversion with a three …

Abstract

Majority of the text modelling techniques yield only point estimates of document embeddings and lack in capturing the uncertainty of the estimates. These uncertainties give a notion of how well the embeddings represent a document. We present Bayesian subspace multinomial model (Bayesian SMM), a generative log-linear model that learns to represent documents in the form of Gaussian distributions, thereby encoding the uncertainty in its covariance. Additionally, in the proposed Bayesian SMM, we address a commonly encountered problem of intractability that appears during variational inference in mixed-logit models. We also present a generative Gaussian linear classifier for topic identification that exploits the uncertainty in document embeddings. Our intrinsic evaluation using perplexity measure shows that the proposed Bayesian SMM fits the data better as compared to variational auto-encoder based document model. Our topic identification experiments on speech (Fisher) and text (20Newsgroups) corpora show that the proposed Bayesian SMM is robust to over-fitting on unseen test data. The topic ID results show that the proposed model is significantly better than variational auto-encoder based methods and achieve similar results when compared to fully supervised discriminative models.

Abstract

In this paper we present a novel weighted NMF (WNMF) algorithm for interpolating missing data. The proposed approach has a computational cost equivalent to that of standard NMF and, additionally, has the flexibility to control the degree of interpolation in the missing data regions. Existing WNMF methods do not offer this capability and, thereby, tend to overestimate the values in the masked regions. By constraining the estimates of the missing-data regions, the proposed approach allows for a better trade-off in the interpolation. We further demonstrate the applicability of WNMF and missing data estimation to the problem of speech enhancement. In this preliminary work, we consider the improvement obtainable by applying the proposed method to ideal binary mask-based gain functions. The instrumental quality metrics (PESQ and SNR) clearly indicate the added benefit of the missing data interpolation, compared …

Abstract

Named Entity Recognition(NER) is one of the important tasks in Natural Language Processing(NLP) and also is a subtask of Information Extraction. In this paper we present our work on NER in Telugu-English code-mixed social media data. Code-Mixing, a progeny of multilingualism is a way in which multilingual people express themselves on social media by using linguistics units from different languages within a sentence or speech context. Entity Extraction from social media data such as tweets(twitter) is in general difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete information. We present a Telugu-English code-mixed corpus with the corresponding named entity tags. The named entities used to tag data are Person(‘Per’), Organization(‘Org’) and Location(‘Loc’). We experimented with the machine learning models Conditional Random Fields(CRFs), Decision Trees and BiLSTMs on our corpus which resulted in a F1-score of 0.96, 0.94 and 0.95 respectively.

Abstract

Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. It is an increasingly common occurrence in today’s multilingual society and poses a big challenge when encountered in different downstream tasks. In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data. Our method consists of three components, each seeking to alleviate different issues. We first generate subword level representations for the sentences using a CNN architecture. The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs - the Collective and Specific Encoder. The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words. This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results - 83.54% accuracy and 0.827 F1 score - on a benchmark dataset.

Abstract

This paper describes a novel, syntax-basedsystem for automatic detection and resolu-tion of Noun Phrase Ellipsis (NPE) in En-glish. The system takes in free input English text, detects the site of nominal elision, and if present, selects potential antecedent candi-dates. The rules are built using the syntactic information on ellipsis and its antecedent dis-cussed in previous theoretical linguistics lit-erature on NPE. Additionally, we prepare acurated dataset of 337 sentences from well-known, reliable sources, containing positiveand negative samples of NPE. We split thisdataset into two parts, and use one part to re-fine our rules and the other to test the perfor-mance of our final system. We get an F1-scoreof 76.47% for detection and 70.27% for NPEresolution on the testset. To the best of ourknowledge, ours is the first system that de-tects and resolves NPE in English. The curateddataset used for this task, albeit small, coversa wide variety of NPE cases and will be madepublic for future work.