Sequence Tagging with Tensorflow | Hacker News Reader

Sequence Tagging with Tensorflow

guillaumegenthial.github.io

98 pointsmelzarei9 comments

I had thought of doing the same thing but POS tagging is already “solved” in some sense by OpenNlp and the Stanford NLP libraries. I think of using deep learning for problems that don’t already have good solutions.

Nice paper, and I look forward to reading the example code.

This does named-entity recognition tagging, which is harder. Also POS results are still not that good on out-of-vocabulary words, so using a word representation that includes sub-word ngrams (like FB Fasttext) can improve results on OOV words.

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...

Don'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.

Disclaimer: 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.

"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.

One can do semi-supervised training with LSTMs, using http://allennlp.org/elmo or similar. That may use huge amounts of data, for which MaxEnt or CRF are not sufficient.

I may be misunderstanding things. Are pronouns not tagged as people? "Obama was the president" vs "I was the president" or "He was the president"

Typically not. Sometimes they are tagged as nominal mentions.

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