1,874 research outputs found
A review of sentiment analysis research in Arabic language
Sentiment analysis is a task of natural language processing which has
recently attracted increasing attention. However, sentiment analysis research
has mainly been carried out for the English language. Although Arabic is
ramping up as one of the most used languages on the Internet, only a few
studies have focused on Arabic sentiment analysis so far. In this paper, we
carry out an in-depth qualitative study of the most important research works in
this context by presenting limits and strengths of existing approaches. In
particular, we survey both approaches that leverage machine translation or
transfer learning to adapt English resources to Arabic and approaches that stem
directly from the Arabic language
Natural language processing for similar languages, varieties, and dialects: A survey
There has been a lot of recent interest in the natural language processing (NLP) community in the computational processing of language varieties and dialects, with the aim to improve the performance of applications such as machine translation, speech recognition, and dialogue systems. Here, we attempt to survey this growing field of research, with focus on computational methods for processing similar languages, varieties, and dialects. In particular, we discuss the most important challenges when dealing with diatopic language variation, and we present some of the available datasets, the process of data collection, and the most common data collection strategies used to compile datasets for similar languages, varieties, and dialects. We further present a number of studies on computational methods developed and/or adapted for preprocessing, normalization, part-of-speech tagging, and parsing similar languages, language varieties, and dialects. Finally, we discuss relevant applications such as language and dialect identification and machine translation for closely related languages, language varieties, and dialects.Non peer reviewe
WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition
We study the problem of evaluating automatic speech recognition (ASR) systems
that target dialectal speech input. A major challenge in this case is that the
orthography of dialects is typically not standardized. From an ASR evaluation
perspective, this means that there is no clear gold standard for the expected
output, and several possible outputs could be considered correct according to
different human annotators, which makes standard word error rate (WER)
inadequate as an evaluation metric. Such a situation is typical for machine
translation (MT), and thus we borrow ideas from an MT evaluation metric, namely
TERp, an extension of translation error rate which is closely-related to WER.
In particular, in the process of comparing a hypothesis to a reference, we make
use of spelling variants for words and phrases, which we mine from Twitter in
an unsupervised fashion. Our experiments with evaluating ASR output for
Egyptian Arabic, and further manual analysis, show that the resulting WERd
(i.e., WER for dialects) metric, a variant of TERp, is more adequate than WER
for evaluating dialectal ASR.Comment: ASRU-201
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