Report on the ACM International Workshop on Methodologies and Evaluation of Lexical Cohesion Techniques in Real-World Applications (ELECTRA 2005) held at SIGIR 2005
Bibliographic record
Abstract
The workshop on Methodologies and Evaluation of Lexical Cohesion Techniques in Real-World Applications (ELECTRA 2005) was held in Salvador, Brazil on August 19 in conjunction with the 28 th ACM Annual International Conference on Research and Development in Information Retrieval (SIGIR 2005). The aim of the workshop was to bring together researchers in NLP and IR to discuss the use of lexical cohesion in text applications, such as document and passage retrieval, question answering, topic segmentation and text summarization. There were a number of related workshops in the past: MEMURA 2004 Workshop on Methodologies and Evaluation of Multiword Units in Real-World Applications in association with the 4th International Conference on Language Resources (LREC 2004) [1], Multiword Expressions: Integrating Processing Workshop in association with the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004) [2], and Multiword Expressions: Analysis, Acquisition and Treatment Workshop in association with the 41st Annual Meeting of the Association for Computational Linguistics (ACL 2003) [3]. The goal of this workshop was to address a wider range of lexical cohesion phenomena in text, not only multiword units, but also relations between words on the sentence, passage and document level, and how they can be useful for information retrieval applications.
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 | 0.062 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".