Extracting Knowledge from an Arabic-English Machine-Readable Dictionary Using Information Extraction
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
arXiv:2606.28457 (cs)
[Submitted on 26 Jun 2026]
Title:Extracting Knowledge from an Arabic-English Machine-Readable Dictionary Using Information Extraction
View a PDF of the paper titled Extracting Knowledge from an Arabic-English Machine-Readable Dictionary Using Information Extraction, by Diaa M. Fayed and 3 other authors
View PDF
Abstract:Natural language processing (NLP) applications need large and rich amount of linguistic knowledge. Furthermore, electronic language sources such as dictionaries, encyclopedia, and corpora became available. So, automatic methods are emerged to extract lexical information from those sources to overcome the knowledge acquisition bottleneck. We presented a method to automatically extract lexical information from a machine-readable version of the Arabic-English Al-Mawrid dictionary. We used n-gram analysis and key-word-in-context (KWIC) analysis to discover lexical patterns that manifest morphologic, syntactic, or semantic information. Then, we used hand-crafted rule-based information extraction to extract that information. Furthermore, we used punctuation marks and some heuristics to extract a set of synonyms in a subentry. This study registered high precision for all types of information, high recall for synonyms, and low recall for the other information. The study also showed that the Al-Mawrid has significant amount of derivations (morphologic information) and synonyms, domain labels, and hyponym/hypernym relations (semantic information).
| Comments: | 9 pages, 7 figures, 4 tables, Conference version,CITALA 2014: 5th International Conference on Arabic Language Processing,Oujda, Morocco, 26-27 November 2014. Paper listed in archived accepted papers: this https URL Original conference site defunct: this http URL No proceedings PDF is publicly available |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.28457 [cs.CL] |
| (or arXiv:2606.28457v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.28457
arXiv-issued DOI via DataCite (pending registration)
|
|
| Journal reference: | Proceedings of CITALA 2014: 5th International Conference on Arabic Language Processing |
Full-text links:
Access Paper:
- View PDF
View a PDF of the paper titled Extracting Knowledge from an Arabic-English Machine-Readable Dictionary Using Information Extraction, by Diaa M. Fayed and 3 other authors
References & Citations
Loading...
Bookmark
Bibliographic Tools
Code, Data, Media
Demos
Related Papers
About arXivLabs
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Generating in the Limit with Infinitely Many Hallucinations
Jun 30
-
Developmental Trajectories of Situation Modeling and Mentalizing in Transformer Language Models
Jun 30
-
A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training
Jun 30
-
Legal Domain Adaptation of Modern BERT Models
Jun 30
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.