353 research outputs found
Generating Navigable Semantic Maps from Social Sciences Corpora
It is now commonplace to observe that we are facing a deluge of online
information. Researchers have of course long acknowledged the potential value
of this information since digital traces make it possible to directly observe,
describe and analyze social facts, and above all the co-evolution of ideas and
communities over time. However, most online information is expressed through
text, which means it is not directly usable by machines, since computers
require structured, organized and typed information in order to be able to
manipulate it. Our goal is thus twofold: 1. Provide new natural language
processing techniques aiming at automatically extracting relevant information
from texts, especially in the context of social sciences, and connect these
pieces of information so as to obtain relevant socio-semantic networks; 2.
Provide new ways of exploring these socio-semantic networks, thanks to tools
allowing one to dynamically navigate these networks, de-construct and
re-construct them interactively, from different points of view following the
needs expressed by domain experts.Comment: in Digital Humanities 2015, Jun 2015, Sydney, Australia. Actes de la
Conf{\'e}rence Digital Humanities 2015. arXiv admin note: text overlap with
arXiv:1406.421
Rule Based Transliteration Scheme for English to Punjabi
Machine Transliteration has come out to be an emerging and a very important research area in the field of machine translation. Transliteration basically aims to preserve the phonological structure of words. Proper transliteration of name entities plays a very significant role in improving the quality of machine translation. In this paper we are doing machine transliteration for English-Punjabi language pair using rule based approach. We have constructed some rules for syllabification. Syllabification is the process to extract or separate the syllable from the words. In this we are calculating the probabilities for name entities (Proper names and location). For those words which do not come under the category of name entities, separate probabilities are being calculated by using relative frequency through a statistical machine translation toolkit known as MOSES. Using these probabilities we are transliterating our input text from English to Punjabi
Hybrid Approach to English-Hindi Name Entity Transliteration
Machine translation (MT) research in Indian languages is still in its
infancy. Not much work has been done in proper transliteration of name entities
in this domain. In this paper we address this issue. We have used English-Hindi
language pair for our experiments and have used a hybrid approach. At first we
have processed English words using a rule based approach which extracts
individual phonemes from the words and then we have applied statistical
approach which converts the English into its equivalent Hindi phoneme and in
turn the corresponding Hindi word. Through this approach we have attained
83.40% accuracy.Comment: Proceedings of IEEE Students' Conference on Electrical, Electronics
and Computer Sciences 201
An Ensemble Model with Ranking for Social Dialogue
Open-domain social dialogue is one of the long-standing goals of Artificial
Intelligence. This year, the Amazon Alexa Prize challenge was announced for the
first time, where real customers get to rate systems developed by leading
universities worldwide. The aim of the challenge is to converse "coherently and
engagingly with humans on popular topics for 20 minutes". We describe our Alexa
Prize system (called 'Alana') consisting of an ensemble of bots, combining
rule-based and machine learning systems, and using a contextual ranking
mechanism to choose a system response. The ranker was trained on real user
feedback received during the competition, where we address the problem of how
to train on the noisy and sparse feedback obtained during the competition.Comment: NIPS 2017 Workshop on Conversational A
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