ikuyamada

7 ms·

1.
Knowledgeable Embedding: Injecting entity knowledge into embedding to boost RAG(github.com)
1 points by ikuyamada 1y ago|0 comments
2.
Luke: New pretrained NLP model achieving SOTA scores on many NLP tasks(github.com)
5 points by ikuyamada 6y ago|0 comments
3.
Neural Attentive Bag-of-Entities Model for Text Classification(github.com)
17 points by ikuyamada 7y ago|0 comments
4.
by ikuyamada 8y ago
My past paper describes an entity linking method based on Wikipedia2Vec: https://arxiv.org/abs/1601.01343 You need to extract entity names using an NER software (e.g., SpaCy, Stanford NER), and resolve the names to kno
5.
by ikuyamada 8y ago
Unlike Word2vec, this tool learns embeddings of entities (i.e., entries in Wikipedia) as well as words. And although the model implemented in this tool is based on Word2vec's skip-gram model, it is extended using two submodels (Wikiped
6.
by ikuyamada 8y ago
You can see the visualization of the embedding vector space here: http://projector.tensorflow.org/?config=https://wikipedia2ve... I recommend to use T-SNE instead of PCA, which can be selected by the button at the
7.
by ikuyamada 8y ago
Author here. More broadly, embedding is a mapping from objects (e.g., words and entities) to vectors of real numbers. And as described in the mcxlog's comment, an entity refers to an entry in Wikipedia in this paper.
8.
by ikuyamada 8y ago
Thank you for your feedback! I am also interested in conducting experiments on extrinsic tasks such as text classification. In addition to word embeddings, Wikipedia2Vec also contains entity embeddings which are likely beneficial for these
9.
by ikuyamada 8y ago
Thanks :) 1) I think learning entity embeddings using the Doc2Vec (paragraph vector) model is an interesting idea, but we did not test it. 2) This tool was initially developed to address the entity linking task. Mapping words and entities i
10.
by ikuyamada 8y ago
Regarding word embedding algorithm, I am interested in supporting other models that uses subword information (e.g., Fasttext). Further, there have been proposed various recent models to learn entity representations from KB, and I plan to wo
11.
by ikuyamada 8y ago
We did not add Fasttext to our benchmarks because of a minor technical issue but we will work on it. Further, to conduct a fair comparison with ELMo, I think it is needed to use extrinsic tasks such as question answering and textual entai
12.
by ikuyamada 8y ago
The current code is written specifically for Wikipedia. However, its algorithm is portable for knowledge bases that contains articles and their entity annotations.
13.
by ikuyamada 8y ago
What kind of output did you mean? Wikipedia2Vec learns embeddings of entities which have links from other articles more than min-entity-count times.
14.
by ikuyamada 8y ago
Please note that similar to other approaches (e.g., node2vec), Wikipedia2Vec learns embeddings for Wikipedia entities in addition to embeddings for words.
15.
Show HN: Wikipedia2Vec – A tool for learning embeddings of words and entities(wikipedia2vec.github.io)
105 points by ikuyamada 8y ago|16 comments
16.
Mongo-memoize(github.com)
1 points by ikuyamada 12y ago|0 comments
17.
Mprpc: A fast Python RPC(github.com)
44 points by ikuyamada 12y ago|2 comments
18.
Gmemcache:Gevent-compatible Memcached Python binary client with consistent hashing(github.com)
1 points by ikuyamada 13y ago|0 comments
19.
by ikuyamada 13y ago
When clicking on the link, Linkify displays a small widget that contains links to typical search sites such as Wikipedia, Google, Twitter, etc.
20.
Show HN: Linkify, auto-linking keywords for easier search on mobile apps(linkify.mobi)
2 points by ikuyamada 13y ago|2 comments
21.
Linkify Harnesses Semantic Technology For Smartphones(inventorspot.com)
2 points by ikuyamada 13y ago|0 comments
22.
Elasticache-auto-discovery(github.com)
2 points by ikuyamada 13y ago|0 comments
23.
Linkify looks to improve mobile search, announces SDK beta for iOS platform(devopsangle.com)
1 points by ikuyamada 13y ago|0 comments
24.
Linkify’s SDK Wants To Make Mobile Searches Less Cumbersome(techcrunch.com)
1 points by ikuyamada 13y ago|0 comments
25.
Dataset: Databases for lazy people(dataset.readthedocs.org)
204 points by ikuyamada 13y ago|49 comments
26.
Aida(github.com)
1 points by ikuyamada 13y ago|0 comments
27.
by ikuyamada 13y ago
In my understanding, DBpedia is a project for extracting data mainly from Wikipedia infoboxes (not whole data dumps) by collaboratively creating rules for converting data into cleaner schema that enables to perform a query such as SPARQL.
28.
Json-wikipedia(github.com)
40 points by ikuyamada 13y ago|16 comments
29.
Dexter(github.com)
2 points by ikuyamada 13y ago|0 comments
30.
WSGI Web Servers Bench(blog.ziade.org)
5 points by ikuyamada 13y ago|0 comments

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