Abstract

In this paper we present our submission for FIRE 2016 Shared Task on Code Mixed Entity Extraction in Indian Languages. We describe a Neural Network system for Entity Extraction in Hindi-English Code Mixed text. Our method uses distributed word representations as features for the Neural Network and therefore, can easily be replicated across languages. Our system ranked first place forHindi-English with an F1-score of 68.24%

Abstract

Internet users today prefer getting precise answers to their questions rather than sifting through a bunch of relevant documents provided by search engines. This has led to the huge popularity of Community Question Answering (cQA) services like Yahoo! Answers, Baidu Zhidao, Quora, Stack Over flowetc., where forum users respond to questions with precise answers. Over time, such cQA archives become rich repositories of knowledge encoded in the form of questions and user generated answers. In cQA archives, retrieval of similar questions, which have already been answered in some form, is important for improving the effectiveness of such forums. The main challenge while retrieving similar questions is the “lexico-syntactic” gap between the user query and the questions already present in the forum. In this paper, we pro-pose a novel approach called “Deep Structured Topic Model (DSTM)” to bridge the lexico-syntactic gap between the question posed by the user and forum questions. DSTM employs a two-step process consisting of initially retrieving similar questions that lie in the vicinity of the query and latent topic vector space and then re-ranking them using a deep layered semantic model. Experiments on large scale real-life cQA dataset show that our approach outperforms the state-of-the-art translation and topic based baseline approaches

Abstract

It has been shown that transition-based methods can be used for syntactic word ordering and tree linearization, achieving significantly faster speed compared with traditional best-first methods. State-of-the-art transition-based models give competitive results on ab-stract word ordering and unlabeled tree lin-earization, but significantly worse results on labeled tree linearization. We demonstrate that the main cause for the performance bottle-neck is the sparsity of SHIFT transition actions rather than heavy pruning. To address this is-sue, we propose a modification to the standard transition-based feature structure, which reduces feature sparsity and allows look ahead features at a small cost to decoding efficiency.Our model gives the best reported accuracies on all benchmarks, yet still being over 30times faster compared with best-first-search.

Abstract

In this paper, we present Kathaa1, anopen source web based Visual Programming Framework for NLP applications. It supports design, execution and analysis of complex NLP systems by choosing and visually connecting NLP modules from an already avail-able and easily extensible Module library. It models NLP systems as a Directed Acyclic Graph of optionally parallalized information flow, and lets the user choose and use avail-able modules in their NLP applications irrespective of their technical proficiency. Kathaa exposes a precise Module definition API to al-low easy integration of external NLP components (along with their associated services as docker containers), it allows everyone to publish their services in a standardized format for everyone else to use it out of the box.

Abstract

Community Question Answering (cQA) forums have become a popular medium for soliciting direct answers to specific questions of users from experts or other experienced users on a given topic. However, for a given question, users some-times have to sift through a large number of low-quality or irrelevant answers to find out the answer which satisfies their information need. To alleviate this, the problem of Answer Quality Prediction (AQP) aims to predict the quality of an answer posted in response to a forum question.Current AQP systems either learn models using - a) various hand-crafted features (HCF) or b) use deep learning (DL)techniques which automatically learn the required feature representations.In this paper, we propose a novel approach for AQP known as -“Deep Feature Fusion Network (DFFN)”which leverages the advantages of both hand-crafted features and deep learning based systems. Given a question-answer pair along with its metadata, DFFN independently - a) learns deep features using a Convolutional Neural Network (CNN) and b) computes hand-crafted features using various external resourcesand then combines them using a deep neural network trained to predict the final answer quality. DFFN achieves state-of-the-art performance on the standard SemEval-2015 andSemEval-2016 benchmark datasets and outperforms base-line approaches which individually employ either HCF or DL based techniques alone.

Abstract

Community Question Answering (cQA)services like Yahoo!Answers1, BaiduZhidao2, Quora3, StackOverflow4etc.provide a platform for interaction with experts and help users to obtain precise and accurate answers to their questions.The time lag between the user posting a question and receiving its answer could be reduced by retrieving similar historic questions from the cQA archives.The main challenge in this task is the “lexico-syntactic” gap between the current and the previous questions. In this paper, we pro-pose a novel approach called“Siamese Convolutional Neural Network for cQA(SCQA)”to find the semantic similarity between the current and the archived ques-tions.SCQA consist of twin convolutional neural networks with shared parameters and a contrastive loss function joining them. SCQA learns the similarity metric for question-question pairs by leveraging the question-answer pairs available in cQA forum archives. The model projects semantically similar question pairs nearer to each other and dissimilar question pairs farther away from each other in the semantic space. Experiments on large scale real-life “Yahoo! Answers” dataset reveals that SCQA outperforms current state-of-the-art approaches based on translation models, topic models and deep neural network based models which use non-shared parameters.

Abstract

Recent literature presents evidence that both linguistic (phonemic) and non linguistic (speaker identity, emotional content) information resides at a lower dimensional manifold embedded richly inside the higher-dimensional spectral features like MFCC and PLP. Linguistic or phonetic units of speech can be broken down to a legal inventory of articulatory gestures shared across several phonemes based on their manner of articulation. We intend to discover a subspace rich in gestural information of speech and captures the invariance of similar gestures. In this paper, we investigate unsupervised techniques best suited for learning such a subspace. Main contribution of the paper is an approach to learn gesture-rich representation of speech automatically from data in completely unsupervised manner. This study compares the representations obtained through convolutional auto encoder (ConvAE) and standard unsupervised dimensionality reduction techniques such as manifold learning and Principal Component Analysis (PCA)through the task of phoneme classification. Manifold learning techniques such as Locally Linear Embedding (LLE), Isomap and Laplacian Eigenmaps are evaluated in this study. The representations which best separate different gestures are suitable for discovering subword units in case of low or zero resource speech conditions. Further, we evaluate the representation using Zero Resource Speech Challenge’s ABX discriminability measure. Results indicate that repre-sentation obtained through ConvAE and Isomap out-perform baseline MFCC features in the task of phoneme classification as well as ABX measure and induce separation between sounds composed of different set of gestures. We further cluster the representations using Dirichlet Process Gaussian Mixture Model (DPGMM) to automatically learn the cluster distribution of data and show that these clusters correspond to groups of similar manner of articulation. DPGMM distribution is used as apriori to obtain correspondence terms for robust ConvAE training

Abstract

In this paper we describe an end to end Neural Model for Named Entity Recognition (NER) which is based on Bi-Directional RNN-LSTM. Almost all NER systems for Hindi use Language Specific features and handcrafted rules with gazetteers. Our model is language independent and uses no domain specific features or any handcrafted rules. Our models rely on semantic information in the form of word vectors which are learnt by an unsupervised learning algorithm on an unannotated corpus. Our model attained state of the art performance in both English and Hindi without the use of any morphological analysis or without using gazetteers of any sort.

Abstract

Recent advancement of social media has led people to share the product reviews through various modalities such as audio, text and video. In this paper, an improved approach to detect the sentiment of an online spoken reviews based on its multi-modality natures (audio and text) is presented. To extract the sentiment from audio, Mel Frequency Cepstral Coefficients(MFCC) features are extracted at stressed significant regions which are detected based on the strength of excitation. Gaussian Mixture Models (GMM) classifier is employed to develop a sentiment model using these features. From results, it is observed that MFCC features extracted at stressed significance regions perform better than the features extracted from the whole audio input. Further from the transcript of the audio input,textual features are computed by Doc2vec vectors. Support Vector Machine (SVM) classifier is used to develop a sentiment model using these textual features. From experimental results it is observed that combining both the audio and text features results in improvement in the performance for detecting the sentiment of a review

Abstract

Community Question Answering (cQA) forums have become a popular medium for soliciting answers to specific user questions from experts and experienced users in a given topic. However,for a given question, users sometimes have to sift through a large number of low-quality or irrelevant answers to find out the answer which satisfies their information need. To alleviate this,the problem of Answer Quality Prediction (AQP) aims to predict the quality of an answer posted in response to a forum question. Current AQP systems either learn models using - a) various hand-crafted features (HCF) or b) Deep Learning (DL) techniques which automatically learn the feature representations.In this paper, we propose a novel approach for AQP known as -“Deep Feature Fusion Network(DFFN)”which combines the advantages of both hand-crafted features and deep learning based systems. Given a question-answer pair along with its metadata, a DFFN architecture indepen-dently - a) learns features using the Deep Neural Network (DNN) and b) computes handcrafted features leveraging various external resources and then combines the musing a fully connected neural network trained to predict the quality of the given answer. DFFN is an end-end differen-tiable model and trained as a single system. We propose two different DFFN architectures which vary mainly in the way they model the input question/answer pair - a) DFFN-CNN which uses a Convolutional Neural Network (CNN) and b) DFFN-BLNA which uses a Bi-directional LSTMwith Neural Attention (BLNA). Both these proposed variants of DFFN (DFFN-CNN and DFFN-BLNA) achieve state-of-the-art performance on the standard SemEval-2015 and SemEval-2016benchmark datasets and outperforms baseline approaches which individually employ either HCFor DL based techniques alone.