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

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

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

Due to increase of online product reviews posted daily through various modalities such as video, audio and text, sentimental analysis has gained huge attention. Recent developments in web technologies have also enabled the increase of web content in Hindi. In this paper, an approach to detect the sentiment of an online Hindi product reviews based on its multi-modality natures (audio and text) is presented. For each audio input, Mel Frequency Cepstral Coefficients (MFCC) features are extracted. These features are used to develop a sentiment models using Gaussian Mixture Models (GMM) and Deep Neural Network (DNN) classifiers. From results, it is observed that DNN classifier gives better results compare to GMM. Further textual features are extracted from the transcript of the audio input by using 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 an online product reviews.

Abstract

Multilingual spoken dialogue systems have gained prominence in the recent past necessitating the requirement for a front-end Language Identification (LID) system. Most of the existing LID systems rely on modeling the language discriminative information from low-level acoustic features. Due tothe variabilities of speech (speaker and emotional variabilities, etc.), large-scale LID systems developed using low-levelacoustic features suffer from a degradation in the performance. In this approach, we have attempted to model the higher level language discriminative phonotactic information for developing an LID system. In this paper, the input speech signal is tokenized to phone sequences by using a language independent phone recognizer. The language discriminative phonotactic information in the obtained phone sequences are modeled using statistical and recurrent neural network based language modeling approaches. As this approach, relies on higher level phonotactical information it is more robust to variabilities of speech. Proposed approach is computationally light weight, highly scalable and it can be used in complement with the existing LID systems

Abstract

Code-Mixing (CM) is defined as the embedding of linguistic units such as phrases, words, and morphemes of one language into an utterance of another language. CM is a natural phenomenon observed in many multilingual societies. It helps in speeding-up communication and allows wider variety of expression due to which it has become a popular mode of communication in social media forums like Facebook and Twitter. However, current Question Answering (QA) re-search and systems only support expressing a question in a single language which is an unrealistic and hard proposition especially for certain domains like health and technology. In this paper, we take the first step towards the development of a full-fledged QA system in CM language which is building a Question Classification (QC) system. The QC system analyzes the user question and infers the expected Answer Type (AType). The AType helps in locating and verifying the answer as it imposes certain type-specific constraints.We learn a basic Support Vector Machine (SVM) based QC system for English-Hindi CM questions. Due to the inherent complexities involved in processing CM language and also the unavailability of language processing resources such POS taggers, Chunkers, Parsers, we design our current system using only word-level resources such as language iden-tification, transliteration and lexical translation. To reducedata sparsity and leverage resources available in a resource-rich language, in stead of extracting features directly from the original CM words, we translate them commonly into English and then perform featurization. We created an evaluation dataset for this task and our system achieves an ac-curacy of 63% and 45% in coarse-grained and fine-grained categories of the question taxonomy. The idea of translating features into English indeed helps in improving accuracy over the uni-gram baseline

Abstract

Sindhi is an Indo-Aryan language spoken by more than 58 million speakers around the world. It is currently a resource poor language which is harmed by the literature being written in multiple scripts. Though the language is widely spoken,primarily, across two countries, the written form is not standardized. In this paper, we seek to develop resources for basic language processing for Sindhi language, in one of its preferred scripts (Devanagari), because a language that seeks to survive in the modern information society requires language technology products. This paper presents our work on building a stochastic Part-of-Speech tagger for Sindhi-Devanagari using conditional random fields with linguistically motivated features. The paper also discusses the steps taken to construct a part-of-speech annotated corpus for Sindhi in Devanagari script. We have also explained in detail the features that were used for training the tagger, which resulted in a part of speech tagger nearing 92% average accuracy.

Abstract

We present a system employing domain specific dictionaries and features to recognize chemical entities. The system utilizes sentence segmentation, tokenization, feature generation, Conditional Random Field (CRF) training and one post-processing step. The dictio-naries were compiled from PubChem, Wikipedia, ChEMBL, DrugBank,word2vec clusters from US patents belonging to A61K class. We report the evaluation results of the run where development set was not included as part of the training set. The best performing model for CEMP taskh as the micro average precision, recall and F-score values of87.26%,79.98%and83.46%, respectively.

Abstract

In this paper, we describe our participation in the 2015 BioASQ challenge on Bio-Medical Question Answering. For Question Answering task (Task 3b), teams were provided with natural language questions and asked to retrieve responses from PubMed corpus in the form of documents, snippets, concepts and RDF triplets (Phase A) and direct answers (Phase B). For Phase A, we took the support of PubMed search engine and our snippet extraction technique. In our QA system, apart from the standard techniques discussed in literature, we tried the following novel techniques to - a) leverage web search results for improving question processing and b) identify domain words and define a new answer ranking function based on number of common domain words. We scored an F-measure of 0.193 for document extraction and F-measure of 0.0717 in snippet generation

Abstract

Automating the process of indexing journal abstracts has been a topic of research for several years. Biomedical Semantic Indexing aims to assign correct MeSH terms to the PubMed documents. In this paper we report our participation in the Task 3a of BioASQ challenge 2015. The participating teams were provided with PubMed articles and asked to return relevant MeSH terms. We tried three different approaches: Nearest Neighbours, IDF-Ratio based indexing and multi-label classification. The official challenge results demonstrate that we consistently performed better than the baseline approaches for Task 3a.