28,211 research outputs found
Performance Analysis of a Part of Speech Tagging Task
This article discusses performance analysis of a part of speech tagging task
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Identifying and Modeling Code-Switched Language
Code-switching is the phenomenon by which bilingual speakers switch between multiple languages during written or spoken communication. The importance of developing language technologies that are able to process code-switched language is immense, given the large populations that routinely code-switch. Current NLP and Speech models break down when used on code-switched data, interrupting the language processing pipeline in back-end systems and forcing users to communicate in ways which for them are unnatural.
There are four main challenges that arise in building code-switched models: lack of code-switched data on which to train generative language models; lack of multilingual language annotations on code-switched examples which are needed to train supervised models; little understanding of how to leverage monolingual and parallel resources to build better code-switched models; and finally, how to use these models to learn why and when code-switching happens across language pairs. In this thesis, I look into different aspects of these four challenges.
The first part of this thesis focuses on how to obtain reliable corpora of code-switched language. We collected a large corpus of code-switched language from social media using a combination of sets of anchor words that exist in one language and sentence-level language taggers. The newly obtained corpus is superior to other corpora collected via different strategies when it comes to the amount and type of bilingualism in it. It also helps train better language tagging models. We also have proposed a new annotation scheme to obtain part-of-speech tags for code-switched English-Spanish language. The annotation scheme is composed of three different subtasks including automatic labeling, word-specific questions labeling and question-tree word labeling. The part-of-speech labels obtained for the Miami Bangor corpus of English-Spanish conversational speech show very high agreement and accuracy.
The second section of this thesis focuses on the tasks of part-of-speech tagging and language modeling. For the first task, we proposed a state-of-the-art approach to part-of-speech tagging of code-switched English-Spanish data based on recurrent neural networks.Our models were tested on the Miami Bangor corpus on the task of POS tagging alone, for which we achieved 96.34% accuracy, and joint part-of-speech and language ID tagging,which achieved similar POS tagging accuracy (96.39%) and very high language ID accuracy (98.78%).
For the task of language modeling, we first conducted an exhaustive analysis of the relationship between cognate words and code-switching. We then proposed a set of cognate-based features that helped improve language modeling performance by 12% relative points. Furthermore, we showed that these features can also be used across language pairs and still obtain performance improvements.
Finally, we tackled the question of how to use monolingual resources for code-switching models by pre-training state-of-the-art cross-lingual language models on large monolingual corpora and fine-tuning them on the tasks of language modeling and word-level language tagging on code-switched data. We obtained state-of-the-art results on both tasks
MultiLexNorm: A Shared Task on Multilingual Lexical Normalization
Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MULTILEXNORM shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 12 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system
MultiLexNorm: A Shared Task on Multilingual Lexical Normalization
Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system
From POS tagging to dependency parsing for biomedical event extraction
Background: Given the importance of relation or event extraction from
biomedical research publications to support knowledge capture and synthesis,
and the strong dependency of approaches to this information extraction task on
syntactic information, it is valuable to understand which approaches to
syntactic processing of biomedical text have the highest performance. Results:
We perform an empirical study comparing state-of-the-art traditional
feature-based and neural network-based models for two core natural language
processing tasks of part-of-speech (POS) tagging and dependency parsing on two
benchmark biomedical corpora, GENIA and CRAFT. To the best of our knowledge,
there is no recent work making such comparisons in the biomedical context;
specifically no detailed analysis of neural models on this data is available.
Experimental results show that in general, the neural models outperform the
feature-based models on two benchmark biomedical corpora GENIA and CRAFT. We
also perform a task-oriented evaluation to investigate the influences of these
models in a downstream application on biomedical event extraction, and show
that better intrinsic parsing performance does not always imply better
extrinsic event extraction performance. Conclusion: We have presented a
detailed empirical study comparing traditional feature-based and neural
network-based models for POS tagging and dependency parsing in the biomedical
context, and also investigated the influence of parser selection for a
biomedical event extraction downstream task. Availability of data and material:
We make the retrained models available at
https://github.com/datquocnguyen/BioPosDepComment: Accepted for publication in BMC Bioinformatic
Low Resource Multi-Task Sequence Tagging -- Revisiting Dynamic Conditional Random Fields
We compare different models for low resource multi-task sequence tagging that
leverage dependencies between label sequences for different tasks. Our analysis
is aimed at datasets where each example has labels for multiple tasks. Current
approaches use either a separate model for each task or standard multi-task
learning to learn shared feature representations. However, these approaches
ignore correlations between label sequences, which can provide important
information in settings with small training datasets. To analyze which
scenarios can profit from modeling dependencies between labels in different
tasks, we revisit dynamic conditional random fields (CRFs) and combine them
with deep neural networks. We compare single-task, multi-task and dynamic CRF
setups for three diverse datasets at both sentence and document levels in
English and German low resource scenarios. We show that including silver labels
from pretrained part-of-speech taggers as auxiliary tasks can improve
performance on downstream tasks. We find that especially in low-resource
scenarios, the explicit modeling of inter-dependencies between task predictions
outperforms single-task as well as standard multi-task models
What can we learn from Semantic Tagging?
We investigate the effects of multi-task learning using the recently
introduced task of semantic tagging. We employ semantic tagging as an auxiliary
task for three different NLP tasks: part-of-speech tagging, Universal
Dependency parsing, and Natural Language Inference. We compare full neural
network sharing, partial neural network sharing, and what we term the learning
what to share setting where negative transfer between tasks is less likely. Our
findings show considerable improvements for all tasks, particularly in the
learning what to share setting, which shows consistent gains across all tasks.Comment: 9 pages with references and appendixes. EMNLP 2018 camera read
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