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Nice article. Explains the whole things really well. I have found LSTM is little overkill for NER, as your entities are limited and data will not be very huge.
by gajju3588 9y ago
Nice article. Explains the whole things really well. I have found LSTM is little overkill for NER, as your entities are limited and data will not be very huge. CRF and Max-Ent seems to better options.
People who want to try it out on some real dataset, can get a sample data for NER from here :
https://dataturks.com/projects/Mohan/Best%20Buy%20E-commerce%20NER%20dataset https://dataturks.com/projects/Mohan/Best%20Buy%20E-commerce...
- Lindrian 9y agoDon't agree. LSTM is a very good option for NER. I have personally experimented with simple FFNN and language models to accommodate variable width input and achieved very good results. I will be publishing a paper on this soon. The system in the article fails at quite simple sentences, such as: "Rex Tillerson is the secretary of state." It tags "Rex Tillerson" as ORG instead of PER. If you change "is" to "was" it tags it as PER.
- melzarei 9y agoDisclaimer: as you may notice, the tagger is far from being perfect. Some errors are due to the fact that the demo uses a reduced vocabulary (lighter for the API). But not all. It is also very sensible to capital letters, which comes both from the architecture of the model and the training data. For more information about the demo, see here.
- bravura 9y ago"I have found LSTM is little overkill for NER, as your entities are limited and data will not be very huge." Incorrect. Inducing word representations on a huge corpus, like Common Crawl, can be more effective than manually building a small Gazeeter of named entities.
- pacala 9y agoOne can do semi-supervised training with LSTMs, using http://allennlp.org/elmo http://allennlp.org/elmo or similar. That may use huge amounts of data, for which MaxEnt or CRF are not sufficient.