A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents (L18-1)
Copied to clipboard
| Challenge: | Terms are notoriously difficult to identify, both automatically and manually. |
| Approach: | They propose a method to annotate terms manually from a comparable corpus . they show that the gold standard provides a tool for evaluation and a rich source of information . |
| Outcome: | The proposed method provides a tool for evaluation and rich source of information about terms. |
Similar Papers
Towards a unified framework for bilingual terminology extraction of single-word and multi-word terms (C18-1)
Copied to clipboard
| Challenge: | Existing methods for extracting bilingual terminology from comparable corpora are limited to a set of syntactic patterns. |
| Approach: | They propose a framework for aligning bilingual terms independently of term lengths . they introduce some enhancements to the context-based and neural network based approaches . |
| Outcome: | The proposed framework improves the performance of the context-based and neural network based approaches and can be adapted in specialized domains. |
Building Comparable Corpora for Assessing Multi-Word Term Alignment (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing methods to extract bilingual terminologies from corpora are limited . MWTs pose serious challenges for alignment and machine translation systems . |
| Approach: | They propose an approach to build comparable corpora and bilingual term dictionaries that evaluate bilingual term alignment in comparable corpus. |
| Outcome: | The proposed method is validated on an existing dataset and manually annotated data. |
AMR Beyond the Sentence: the Multi-sentence AMR corpus (C18-1)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) is limited to capturing the semantics of individual sentences. |
| Approach: | They propose a corpus that annotates coreference and similar phenomena on top of existing AMRs. |
| Outcome: | The proposed corpus is compared with existing corpora on sentence-level semantics . it shows that it can be used for information extraction and question answering . |
A Multilingual Dataset for Evaluating Parallel Sentence Extraction from Comparable Corpora (L18-1)
Copied to clipboard
| Challenge: | BUCC Shared Task aims to extract parallel sentences from comparable corporad . resulting corpus contains about 3.5 million distinct sentences in english, french, german, Russian, and Chinese . |
| Approach: | They present challenges faced to build a parallel sentences dataset from comparable corporad . they emphasize issues faced to include Chinese as one of the languages . |
| Outcome: | The 2017 BUCC Shared Task was a first for this task . the dataset contains 3.5 million sentences in English, French, German, Russian, and Chinese . |
The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)
Copied to clipboard
| Challenge: | Until recently, language descriptions were available in paper form only, with indexes as the only search aid. |
| Approach: | They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful. |
| Outcome: | The proposed corpus is searchable through a couple of well-established corpus infrastructures. |
Predicting Degrees of Technicality in Automatic Terminology Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | a recent study has focused on term technicality, but there are still few studies on it. |
| Approach: | They semi-automatically create a German gold standard of technicality across four domains . they propose two new models to exploit general- vs. domain-specific comparisons based on vector spaces . |
| Outcome: | The proposed model outperforms previous methods in terms of general- vs. domain-specific comparisons. |
Automatic Annotation of Semantic Term Types in the Complete ACL Anthology Reference Corpus (L18-1)
Copied to clipboard
| Challenge: | a recent increase in quantitative studies of scientific text collections has led to a significant increase in the use of semantic labeling techniques. |
| Approach: | They propose to use semantic class labels to enhance a well-known resource . they use semantic labels to assign semantic class labeling to technical terms . |
| Outcome: | The proposed approach enhances the ACL Anthology Reference Corpus with semantic class labels for 20,000 technical terms . the goal is to use this information as one feature in the profiling of scientific papers, communities, and disciplines. |
Cross-lingual Terminology Extraction for Translation Quality Estimation (L18-1)
Copied to clipboard
| Challenge: | Using common statistical measures for termhood and unithood, we identify terms from monolingual texts and investigate the contribution of terminology to translation quality. |
| Approach: | They propose to use common statistical measures for termhood and unithood as features to train classifiers for identifying terms in cross-domain and cross-language settings. |
| Outcome: | The proposed method has shown some reliability in automatically identifying terms in human translations, but drawbacks in handling low frequency terms and term variations shall be dealt with in the future. |
Multilingualization of Medical Terminology: Semantic and Structural Embedding Approaches (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for multilingual terminology curation are limited as they do not fit the term within existing terminology. |
| Approach: | They propose a method to encode the structural property of a term by aligning embeddings using graph convolutional networks trained from separate languages. |
| Outcome: | The proposed method can encode the structural property of a term by aligning embeddings using graph convolutional networks trained from separate languages. |
Crossing Domains without Labels: Distant Supervision for Term Extraction (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. |
| Approach: | They propose a benchmark spanning seven diverse domains to evaluate ATE performance . they propose psuedo-labels and post-hoc heuristics to ensure generalizability . |
| Outcome: | The proposed model outperforms supervised cross-domain encoder models and few-shot learning baselines on the document- and corpus-levels and its GPT-4o teacher on the benchmark. |