Proceedings of the Fifth Named Entity Workshop
Bibliographic record
Abstract
The purpose of the NEWS workshop series is to bring together researchers across the world interested in identification, analysis, extraction, mining and transformation of named entities in monolingual or multilingual natural language text corpora.The workshop scope includes many interesting specific research areas pertaining to the named entities, such as, orthographic and phonetic characteristics, corpus analysis, unsupervised and supervised named entities extraction in monolingual or multilingual corpus, transliteration modeling, and evaluation methodologies, to name a few.For this year edition, 5 research papers were submitted, each paper was reviewed by at least 2 reviewers from the program committee.The 5 papers were all chosen for publication, covering named entity recognition and machine transliteration, which applied various new trend methods such as deep neural networks and graph-based semi-supervised learning.Following the tradition of the NEWS workshop series, NEWS 2015 continued the machine transliteration shared task this year as well.The shared task was first introduced in NEWS 2009 and continued in NEWS 2010, NEWS 2011, and NEWS 2012.In NEWS 2015, by leveraging on the previous success of NEWS workshop series, we released the hand-crafted parallel named entities corpora to include 14 different language pairs from 12 language families, and made them available as the common dataset for the shared task.In total, 7 international teams participated from around the globe, while one team withdrew their results at the evaluation phase.Finally, we received 6 teams' submissions.The approaches ranged from traditional learning methods (such as, Phrasal SMT-based, Conditional Random Fields, etc.) to somewhat new approaches (such as, neural network transduction, integration of transliteration mining, hybrid system combination).A concrete study and targeted process between two languages often generate better performances.A report of the shared task that summarizes all submissions and the original whitepaper are also included in the proceedings, and will be presented in the workshop.The participants in the shared task were asked to submit short system papers (4 content pages each) describing their approaches, and each of such papers was reviewed by at least two members of the program committee to help improve the quality.All the 6 system papers were finally accepted to be published in the workshop proceedings.We hope that NEWS 2015 would provide an exciting and productive forum for researchers working in this research area, and the NEWS-released data continues to serve as a standard dataset for machine transliteration generation and mining.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.091 | 0.066 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".