3,723 research outputs found
PersoNER: Persian named-entity recognition
Š 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network
Access to recorded interviews: A research agenda
Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed
Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization
Existing methods for CWS usually rely on a large number of labeled sentences
to train word segmentation models, which are expensive and time-consuming to
annotate. Luckily, the unlabeled data is usually easy to collect and many
high-quality Chinese lexicons are off-the-shelf, both of which can provide
useful information for CWS. In this paper, we propose a neural approach for
Chinese word segmentation which can exploit both lexicon and unlabeled data.
Our approach is based on a variant of posterior regularization algorithm, and
the unlabeled data and lexicon are incorporated into model training as indirect
supervision by regularizing the prediction space of CWS models. Extensive
experiments on multiple benchmark datasets in both in-domain and cross-domain
scenarios validate the effectiveness of our approach.Comment: 7 pages, 11 figures, accepted by the 2019 World Wide Web Conference
(WWW '19
Benchmarking Arabic AI with Large Language Models
With large Foundation Models (FMs), language technologies (AI in general) are
entering a new paradigm: eliminating the need for developing large-scale
task-specific datasets and supporting a variety of tasks through set-ups
ranging from zero-shot to few-shot learning. However, understanding FMs
capabilities requires a systematic benchmarking effort by comparing FMs
performance with the state-of-the-art (SOTA) task-specific models. With that
goal, past work focused on the English language and included a few efforts with
multiple languages. Our study contributes to ongoing research by evaluating FMs
performance for standard Arabic NLP and Speech processing, including a range of
tasks from sequence tagging to content classification across diverse domains.
We start with zero-shot learning using GPT-3.5-turbo, Whisper, and USM,
addressing 33 unique tasks using 59 publicly available datasets resulting in 96
test setups. For a few tasks, FMs performs on par or exceeds the performance of
the SOTA models but for the majority it under-performs. Given the importance of
prompt for the FMs performance, we discuss our prompt strategies in detail and
elaborate on our findings. Our future work on Arabic AI will explore few-shot
prompting, expand the range of tasks, and investigate additional open-source
models.Comment: Foundation Models, Large Language Models, Arabic NLP, Arabic Speech,
Arabic AI, , CHatGPT Evaluation, USM Evaluation, Whisper Evaluatio
Multi-task Pairwise Neural Ranking for Hashtag Segmentation
Hashtags are often employed on social media and beyond to add metadata to a
textual utterance with the goal of increasing discoverability, aiding search,
or providing additional semantics. However, the semantic content of hashtags is
not straightforward to infer as these represent ad-hoc conventions which
frequently include multiple words joined together and can include abbreviations
and unorthodox spellings. We build a dataset of 12,594 hashtags split into
individual segments and propose a set of approaches for hashtag segmentation by
framing it as a pairwise ranking problem between candidate segmentations. Our
novel neural approaches demonstrate 24.6% error reduction in hashtag
segmentation accuracy compared to the current state-of-the-art method. Finally,
we demonstrate that a deeper understanding of hashtag semantics obtained
through segmentation is useful for downstream applications such as sentiment
analysis, for which we achieved a 2.6% increase in average recall on the
SemEval 2017 sentiment analysis dataset.Comment: 12 pages, ACL 201
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