Anchors
| Anchor text | Ref. domains ▾ | Top DR | Ref. pages | Links to target | Dofollow links |
|---|---|---|---|---|---|
| 31 | — | 0 | 119 | 119 100% | |
| ACL | 23 | — | 0 | 37 | 34 91.9% |
| 23 | — | 0 | 180 | 180 100% | |
| Association for Computational Linguistics | 21 | — | 0 | 21 | 20 95.2% |
| paper | 16 | — | 0 | 65 | 48 73.8% |
| ACL Anthology | 15 | — | 0 | 26 | 26 100% |
| 13 | — | 0 | 115 | 115 100% | |
| URL | 8 | — | 0 | 117 | 117 100% |
| Link | 8 | — | 0 | 14 | 13 92.9% |
| here | 6 | — | 0 | 9 | 9 100% |
| [pdf] | 6 | — | 0 | 16 | 16 100% |
| Paper | 6 | — | 0 | 10 | 10 100% |
| http | 4 | — | 0 | 11 | 11 100% |
| aclweb.org | 4 | — | 0 | 8 | 8 100% |
| Association for Computational Linguistics (ACL) | 4 | — | 0 | 9 | 9 100% |
| [PDF] | 4 | — | 0 | 19 | 19 100% |
| bib | 4 | — | 0 | 94 | 94 100% |
| [Paper] | 3 | — | 0 | 4 | 4 100% |
| RTE | 3 | — | 0 | 6 | 6 100% |
| Convolutional Neural Networks for Sentence Classification | 2 | — | 0 | 2 | 2 100% |
| A Constituent-Centric Neural Architecture for Reading Comprehension | 2 | — | 0 | 2 | 2 100% |
| [link] | 2 | — | 0 | 5 | 5 100% |
| Controlled and Balanced Dataset for Japanese Lexical Simplification | 2 | — | 0 | 2 | 2 100% |
| Slides | 2 | — | 0 | 2 | 2 100% |
| ACL Anti-Harassment Policy | 2 | — | 0 | 3 | 3 100% |
| Integrating Multimodal Information in Large Pretrained Transformers | 2 | — | 0 | 2 | 2 100% |
| ACL Author Guidelines | 2 | — | 0 | 2 | 2 100% |
| "Resources Report on Languages of Indonesia" | 2 | — | 0 | 2 | 1 50% |
| Findings of the 2020 Conference on Machine Translation (WMT20) | 2 | — | 0 | 2 | 1 50% |
| Controllable Text Simplification with Lexical Constraint Loss | 2 | — | 0 | 2 | 2 100% |
| Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games | 2 | — | 0 | 2 | 2 100% |
| "ACL Lifetime Achievement Award Recipients" | 2 | — | 0 | 3 | 0 0% |
| Text Classification with Negative Supervision | 2 | — | 0 | 2 | 2 100% |
| Workshop Proceedings | 2 | — | 0 | 2 | 2 100% |
| ACL anthology | 2 | — | 0 | 3 | 3 100% |
| A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems | 2 | — | 0 | 2 | 2 100% |
| Unification-based Multimodal Parsing | 2 | — | 0 | 2 | 1 50% |
| Building a Monolingual Parallel Corpus for Text Simplification Using Sentence Similarity Based on Alignment between Word Embeddings | 2 | — | 0 | 2 | 2 100% |
| Electronic Edition | 2 | — | 0 | 4 | 4 100% |
| Target-dependent Twitter Sentiment Classification | 2 | — | 0 | 2 | 2 100% |
| ACL anti-harassment policy | 2 | — | 0 | 3 | 3 100% |
| electronic edition @ aclweb.org | 2 | — | 0 | 6 | 6 100% |
| Negative Lexically Constrained Decoding for Paraphrase Generation | 2 | — | 0 | 2 | 2 100% |
| 3 | 2 | — | 0 | 2 | 2 100% |
| https://www.aclweb.org/anthology/L16-1107.pdf | 2 | — | 0 | 3 | 3 100% |
| https://www.aclweb.org/anthology/2002.eamt-1.11.pdf | 2 | — | 0 | 2 | 2 100% |
| CoNLL-2003 Named Entity Recognition | 2 | — | 0 | 2 | 1 50% |
| Incremental Skip-gram Model with Negative Sampling | 2 | — | 0 | 2 | 2 100% |
| Tiny Word Embeddings Using Globally Informed Reconstruction | 2 | — | 0 | 2 | 2 100% |
| ACL Code of Ethics | 2 | — | 0 | 2 | 2 100% |
Frequently Asked Questions
What anchor texts are used to link to aclweb.org?
This page shows all anchor texts found in backlinks pointing to aclweb.org, sorted by the number of referring domains using each anchor. Anchor texts range from branded terms (like the domain name itself) to keyword-rich phrases that describe the linked content. The distribution of anchor texts reveals how other websites perceive and describe aclweb.org.
What is anchor text?
Anchor text is the visible, clickable text in a hyperlink. Search engines use anchor text as a signal to understand what the linked page is about. For example, if many sites link to a page using the anchor text "best running shoes," search engines infer that the page is relevant to that topic. Anchor text appears in several forms: exact-match (contains target keywords), branded (uses the company or domain name), generic (like "click here"), and naked URLs.
Why is anchor text analysis important for SEO?
Anchor text analysis helps identify potential SEO risks and opportunities. A natural backlink profile has diverse anchor texts including branded terms, generic phrases, and topic-relevant keywords. Over-optimization, where too many backlinks use the same exact-match keyword anchor, can trigger search engine penalties. Conversely, understanding which anchors drive the most authority (measured by referring domain count and DR) helps prioritize link building efforts.
How many unique anchor texts does aclweb.org have?
The anchor text report for aclweb.org displays all distinct anchor texts grouped by their hash. Each row shows how many unique referring domains use that anchor, the total number of links, and the dofollow percentage. A high number of unique anchors generally indicates a healthy, natural backlink profile with diverse link sources.