41 |
Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)
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42 |
Developing multilingual automatic semantic annotation systems
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43 |
A Sense Annotated Corpus for All-Words Urdu Word Sense Disambiguation
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44 |
CLEU- A Cross-Language-Urdu Corpus and Benchmark For Text Reuse Experiments
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45 |
Multilingual Financial Narrative Processing:Analysing Annual Reports in English, Spanish and Portuguese
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46 |
Enhancing the linguistic discovery potential of historical corpora:A twin-track approach using ARCHER
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47 |
Classifying Information Sources in Arabic Twitter to Support Online Monitoring of Infectious Diseases
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49 |
FIESTA:Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms
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50 |
Open Welsh Language Resources for a Corpus Annotation Framework
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52 |
How do people describe personal recovery experiences in bipolar disorder in structured and informal settings?
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53 |
In Search of Meaning:Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse
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56 |
Towards a Multilingual Financial Narrative Processing System
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57 |
Profiling Medical Journal Articles Using a Gene Ontology Semantic Tagger
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58 |
Bringing replication and reproduction together with generalisability in NLP:Three reproduction studies for Target Dependent Sentiment Analysis
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Abstract:
Lack of repeatability and generalisability are two significant threats to continuing scientific development in Natural Language Processing. Language models and learning methods are so complex that scientific conference papers no longer contain enough space for the technical depth required for replication or reproduction. Taking Target Dependent Sentiment Analysis as a case study, we show how recent work in the field has not consistently released code, or described settings for learning methods in enough detail, and lacks comparability and generalisability in train, test or validation data. To investigate generalisability and to enable state of the art comparative evaluations, we carry out the first reproduction studies of three groups of complementary methods and perform the first large-scale mass evaluation on six different English datasets. Reflecting on our experiences, we recommend that future replication or reproduction experiments should always consider a variety of datasets alongside documenting and releasing their methods and published code in order to minimise the barriers to both repeatability and generalisability. We have released our code with a model zoo on GitHub with Jupyter Notebooks to aid understanding and full documentation, and we recommend that others do the same with their papers at submission time through an anonymised GitHub account.
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URL: https://eprints.lancs.ac.uk/id/eprint/125854/4/bringing_replication_reproduction.pdf https://eprints.lancs.ac.uk/id/eprint/125854/ https://eprints.lancs.ac.uk/id/eprint/125854/1/bringing_replication_reproduction.pdf
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59 |
Increasing Interoperability for Embedding Corpus Annotation Pipelines in Wmatrix and other corpus retrieval tools
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60 |
Arabic Dialect Identification in the Context of Bivalency and Code-Switching
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