Entity linking for English and other languages: a survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Extracting named entities text forms the basis for many crucial tasks such as information retrieval and extraction, machine translation, opinion mining, sentiment analysis and question answering. This paper presents a survey of the research literature on named entity linking, including named entity recognition and disambiguation. We present 200 works by focusing on 43 papers (5 surveys and 38 research works). We also describe and classify 56 resources, including 25 tools and 31 corpora. We focus on the most recent papers, where more than 95% of the described research works are after 2015. To show the efficiency of our construction methodology and the importance of this state of the art, we compare it to other surveys presented in the research literature, which were based on different criteria (such as the domain, novelty and presented models and resources). We also present a set of open issues (including the dominance of the English language in the proposed studies and the frequent use of NER rather than the end-to-end systems proposing NED and EL) related to entity linking based on the research questions that this survey aims to answer.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it