Papers with Conditional Random Fields
Sentence Suggestion of Japanese Functional Expressions for Chinese-speaking Learners (P18-4)
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| Challenge: | a large number of Chinese characters are commonly used both in Chinese and Japanese. |
| Approach: | They propose a computer-assisted learning system for Chinese-speaking learners of Japanese as a second language (JSL) they use a free Japanese morphological analyzer MeCab to learn Japanese functional expressions with suggestion of appropriate example sentences. |
| Outcome: | The proposed system automatically recognizes Japanese functional expressions using a free Japanese morphological analyzer and is retrained on a new conditional random field model. |
Multi-Dialect Arabic POS Tagging: A CRF Approach (L18-1)
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Kareem Darwish, Hamdy Mubarak, Ahmed Abdelali, Mohamed Eldesouki, Younes Samih, Randah Alharbi, Mohammed Attia, Walid Magdy, Laura Kallmeyer
| Challenge: | Existing work on dialectal POS tagging is rather scant with POS tags for most dialects being nonexistent or of limited availability. |
| Approach: | They propose a dataset of POS-tagged Arabic tweets in four major dialects and a tagging guideline for each dialect. |
| Outcome: | The proposed model can tag four different dialects with an average accuracy of 89.3%. |
Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text (D19-62)
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| Challenge: | Several ethical and scientific issues arise regarding the balance between maintaining patient confidentiality and the need for wider application of trained models. |
| Approach: | They propose to use pseudonymised clinical text as training data to de-identify real clinical text in other hospitals. |
| Outcome: | The proposed model performed better for some PHI information than the standard model and poor performance on Location and Health Care Unit information. |
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019 (D19-57)
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| Challenge: | Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature. |
| Approach: | They propose to use Named Entities to perform nested entities extraction, Entity Normalization and Relation Extraction to generalize the approach to different languages. |
| Outcome: | The proposed approach can be generalized to different languages and showed it’s effectiveness for English and Spanish text. |
Negative Focus Detection via Contextual Attention Mechanism (D19-1)
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| Challenge: | Negation is a universal but complicated linguistic phenomenon that reverses the polarity of a statement or its property into opposite. |
| Approach: | They propose a framework which consists of a Bidirectional Long Short-Term Memory neural network and a Conditional Random Fields layer to capture contextual information. |
| Outcome: | The proposed framework improves on the SEM’12 shared task corpus, yielding an absolute improvement of 2.11% over the state-of-the-art. |
Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies (2022.naacl-main)
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| Challenge: | Existing models that perform information extraction tasks manually assume heuristic dependency between the task instances and mean-field factorization for the joint distribution of instance labels. |
| Approach: | They propose to induce a dependency graph among task instances to boost representation learning by estimating their joint distribution via Conditional Random Fields. |
| Outcome: | The proposed model outperforms previous models on multiple IE tasks across 5 datasets and 2 languages. |
Pretrained Language Models for Sequential Sentence Classification (D19-1)
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| Challenge: | Recent successful models for document-level understanding have used hierarchical encoding and CRFs to capture dependencies between subsequent labels. |
| Approach: | They propose a pretrained language model that captures contextual dependencies without hierarchical encoding nor a CRF. |
| Outcome: | The proposed model captures contextual dependencies without hierarchical encoding nor a CRF on four datasets, including a new dataset of structured scientific abstracts. |
Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction (2021.acl-long)
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| Challenge: | Experimental results demonstrate the effectiveness of Tail-to-Tail (TtT) non-autoregressive sequence prediction for Chinese Grammatical Error Correction (CGEC) |
| Approach: | They propose a framework for Chinese Grammatical Error Correction (CGEC) that uses a BERT-initialized Transformer Encoder to model the error positions. |
| Outcome: | The proposed framework solves the problem of Chinese Grammatical Error Correction (CGEC) by modeling the token dependencies. |
Enhancing Deep Learning with Embedded Features for Arabic Named Entity Recognition (2022.lrec-1)
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| Challenge: | Word embeddings can capture the semantics of words and other hidden features, but the Arabic language is complex and requires a large amount of information to process. |
| Approach: | They propose to add morphological and syntactical features to Arabic word embeddings to train the model. |
| Outcome: | The proposed model outperforms the previous systems to the best of our knowledge. |
Portuguese Named Entity Recognition using Conditional Random Fields and Local Grammars (L18-1)
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| Challenge: | Named Entity Recognition (NER) involves automatically identifying and classifying entities such as persons, places, organizations and values. |
| Approach: | They propose to use Conditional Random Fields for Named Entity Recognition in Portuguese texts using a Local Grammar as an additional informed feature. |
| Outcome: | The proposed method outperforms competing systems in the literature. |
On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation (2024.lrec-main)
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| Challenge: | a new study examines the use of encoder-only pre-trained language models in keyphrase generation (KPG) keyphrases are phrases that condense salient information of a document. |
| Approach: | They propose to use encoder-only pre-trained language models in keyphrase generation . they also examine optimal architectural decisions for employing encoder only PLMs in KPG . |
| Outcome: | The proposed model outperforms general-domain seq2seq models in keyphrase generation. |