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Month: January 2017

Attending to characters in neural sequence labeling models

Marek 7 Comments

Word embeddings are great. They allow us to represent words as distributed vectors, such that semantically and functionally similar words have similar representations. Having similar vectors means these words also behave similarly in the model, which is what we want for good generalisation properties.

However, word embeddings have a couple of weaknesses:

  1. If a word doesn’t exist in the training data, we can’t have an embedding for it. Therefore, the best we can do is clump all unseen words together under a single OOV (out-of-vocabulary) token.
  2. If a word only occurs a couple of times, the word embedding likely has very poor quality. We simply don’t have enough information to learn how these words behave in different contexts.
  3. We can’t properly take advantage of character-level patterns. For example, there is no way to learn that all words ending with -ing are likely to be verbs. The best we can do is learn this for each word separately, but that doesn’t help when faced with new or rare words.

In this post I will look at different ways of extending word embeddings with character-level information, in the context of neural sequence labeling models.  You can find more information in the Coling 2016 paper “Attending to characters in neural sequence labeling models“.

Sequence labeling

We’ll investigate word representations in order to improve on the task of sequence labeling. In a sequence labeling setting, a system gets a series of tokens as input and it needs to assign a label to every token. The correct label typically depends on both the context and the token itself. Quite a large number of NLP tasks can be formulated as sequence labeling, for example:

POS-tagging
DT  NN    VBD      NNS    IN      DT   DT  NN     CC  DT  NN   .
The pound extended losses against both the dollar and the euro .

Error detection
+ +    +  x       +   +      +   +    +    x      +
I like to playing the guitar and sing very louder .

Named entity recognition
PER _      _   _      _  ORG  ORG   _  TIME _
Jim bought 300 shares of Acme Corp. in 2006 .

Chunking
B-NP    B-PP B-NP I-NP B-VP I-VP     I-VP I-VP   B-PP B-NP B-NP  O
Service on   the  line is   expected to   resume by   noon today .

In each of these cases, the model needs to understand how a word is being used in a specific context, and could also take advantage of character-level patterns and morphology.

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NLP and ML Publications – Looking Back at 2016

Marek 3 Comments

After my last post on analysing publication patterns I received quite a lot of feedback and many feature requests, so I decided to create an update once 2016 is over. It is now quite a bit bigger than before, and includes 11 different conferences and journals: ACL, EACL, NAACL, EMNLP, COLING, CL, TACL, CoNLL, *Sem+SemEval, NIPS, and ICML.

The information used in these graphs was collected through crawling the web. ACL Anthology was very useful, listing papers in a consistent format. However, information such as the organisation names in each paper still needed to be extracted directly from the pdfs, which means there are likely to be some errors. I’ve tried to create exceptions to catch different spelling variations and other anomalies, but if you notice mistakes in the graphs, do let me know.

This analysis shouldn’t be taken too seriously – after all, quality of research matters much more than quantity, and that is considerably more difficult to measure. However, my motivation is to provide a high-level overview of what is happening in the field, where the big players are publishing, and perhaps supply a bit of inspiration and motivation for the new year.

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