natural language processing (almost) from scratch.

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Benchmark Tasks

Four standard NLP tasks were chosen for testing and comparison with other systems: part of speech labelling (POS), chunking (CHUNK), named entity recognition (NER), and semantic role labelling (SRL).

Part of Speech Labelling (POS)

The goal of part of speech labelling is to tag each word with its semantic role (verb, plural noun, adverb, etc.) The experimental setup is borrowed from Toutonova et. al (2003). Sections 0-18 of the Wall Street Journal (WSJ) data are used for training, sections 19-21 for validation, and sections 22-24 for testing.

Chunking (CHUNK)

Chunking (CHUNK) or shallow parsing is intended to break a sentence into its syntactic components (noun phrase, verb phrase, etc.). Each word is tagged with the single component it belongs to, sometimes encoded with separate tags for a word at the beginning of the component and a word in the middle of the component. Experimental setup was taken from the CONLL 2003 task and uses WSJ data.

Named Entity Recognition (NER)

In named entity recognition, words are tagged with a relevant category such as "person" or "location". Experimental setup is taken from the relevant CONL task and uses Reuters data.

Semantic Role Labelling (SRL)

SRL aims to give a semantic role to a component of a sentence (for example, identifying predicates of a verb). CoNLL 2005

Network Design

Words are preprocessed by lower-casing and encoding capitalization as an additional feature.

Notation and Hyper-Parameters

[math]\displaystyle{ \,f_\theta }[/math](.) denotes any neural network with parameters [math]\displaystyle{ \,\theta }[/math]. Any feed-forward neural network with L layers can be written as a composition of functions as follows: [math]\displaystyle{ \,f_\theta = f_\theta^L(f_\theta^{L-1}(...f_\theta^1(.)...)) }[/math].

[math]\displaystyle{ \,[A]_{i,j} }[/math] denotes the element of a matrix A at row i and column j.

[math]\displaystyle{ \,\langle A\rangle_i^{d_{win}} }[/math] denotes the matrix obtained by concatenating the d_win columns vectors around the i^th column vector of a matrix A ∈ R^d₁xd₂. i.e., [math]\displaystyle{ \,[\langle A\rangle_i^{d_{win}}]^T = ([A]_{1,i-d_{win}/2} ... [A]_{d_1,i-d_{win}/2},...., [A]_{1,i+d_{win}/2} ... [A]_{d_1,i+d_{win}/2}) }[/math].

[math]\displaystyle{ \,\langle A\rangle_i^1 }[/math] is a special case which denotes the i^th column vector of A.

[math]\displaystyle{ \,[x]_1^T }[/math] denotes the sequences of elements [math]\displaystyle{ \left\{ x_1, x_2, ... , x_T \right\} }[/math] and [math]\displaystyle{ [x]_i }[/math] denotes the i^th element of the sequence.

[math]\displaystyle{ \,d_{wrd} }[/math] is the word vector size, given by the user.

[math]\displaystyle{ \,d_{wrd}^k }[/math] is the vector size for a feature k, given by the user.

[math]\displaystyle{ \,k_{sz} }[/math] is the window size, given by the user.

[math]\displaystyle{ \,n_{hu}^l }[/math] is the number of hidden units for the l^th layer, given by the user.

Feature Vectors

Words are fed into the network as indices in a finite dictionary D. The first layer of the network maps each of the word indices to a feature vector.

For each word w ∈ D the lookup table layer LT_w(.) gives a d_wrd-dimensional feature vector representation:

[math]\displaystyle{ LT_W(.) = \langle W\rangle_w^1 }[/math]

Where W ∈ R^d_wrdx|D|. is a matrix of parameters to be learnt.

For a sequence of words [math]\displaystyle{ \,[x]_1^T }[/math], the lookup table layer produces the following output matrix:

[math]\displaystyle{ LT_W([w]_1^T) = (\langle W\rangle_{[w]_1}^1 \langle W\rangle_{[w]_2}^1) ... \langle W\rangle_{[w]_T}^1) }[/math]

To add additional features, we can represent the word as K discrete features w ∈ D₁×...×D_k, where D_k is the dictionary for the k^th feature. Each feature is associated with a lookup table LT_w^k(.) with parameters W^k ∈ R^d_wrdx|D|, and the outputs are concatenated to form the final feature vector.

[math]\displaystyle{ LT_{w^1,...,w^K}(w)= \left( \begin{array}{c} LT_{w^1}(w_1) \\ \vdots \\ LT_{w^2}(w_2)\end{array} \right) = \left( \begin{array}{c} \langle W\rangle_{[w]_1}^1 \\ \vdots \\ \langle W\rangle_{[w]_K}^1 \end{array} \right) }[/math]

Windowed Approach

This approach assumes the tag for a word depends mostly on its surrounding words. Given a word to be tagged, we feed a window of k_sz words around the word of interest to the lookup table layer, which outputs a matrix of size d_wrd×k_sz. Finally, the columns of this output matrix are concatenated to use as input to the next layer.

[math]\displaystyle{ f_\theta^1 = \langle LT_W([w]_1^T) \rangle _t ^{d_{win}} = \left( \begin{array}{c} \langle W\rangle_{[w]_{t-d_{win}/2}}^1 \\ \vdots \\ \langle W\rangle_{[w]_{t}}^1 \\ \vdots \\ \langle W\rangle_{[w]_{t+d_{win}/2}}^1 \end{array} \right) }[/math]

The output from the first layer is fed to one or more linear layers:

[math]\displaystyle{ f_\theta^l = \langle W \rangle^l f_\theta^{l-1} + b^l }[/math]

Where [math]\displaystyle{ \,W^l \in R^{n_{hu}^l × n_{hu}^l-1} }[/math] and [math]\displaystyle{ b^l \in R^{n_{hu}^l} }[/math] are parameters to be trained.

Linear layers may be interleaved with layers which apply a hard hyperbolic tangent function over their inputs:

[math]\displaystyle{ \left[f_\theta^l\right]_i = HardTanh(\left[f_\theta^{l-1} \right]_i) }[/math]

Where HardTanh is defined as:

[math]\displaystyle{ HardTanh(x) = \left\{ \begin{array}{l} -1\ if\ x \lt -1 \\ x\ if\ -1 \leq x \leq 1 \\ 1\ if\ x \gt 1 \end{array} \right. }[/math]

Finally, the output size of the last layer is the number of tags for a particular task, and each output entry can be interpreted as a score for that tag.

Sentence-Level Approach

The windowed approach described above is appropriate for tasks where only information about the surrounding words is needed, but is not appropriate for tasks like SRL where the appropriate tag for a word may depend on the whole sentence. A convolutional approach is taken to solve this problem.

The convolutional network produces features for each word in the sentence and for each verb in the sentence. For each word i, two extra features are added which encode the distance between i and the word to tag (i - pos_w), and i and the verb being considered (i - pos_v).

References

R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu and P. Kuksa. Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research, 12:2493-2537, 2011. K. Toutanova, D. Klein, C. D. Manning, and Y. Singer. Feature-rich part-of-speech taggingwith a cyclic dependency network. InHLT-NAACL, 2003.