This example illustrates the simplicity of sequence tagging because (1) shallow syntactic analysis reduces the precision requirement of syntactic analysis; (2) pruning candidate arguments is removed; 3) argument identification and tagging are finished at the same time. Such unified methods simplify the procedure, reduce the risk of accumulating errors and boost the performance further.
In this tutorial, our SRL system is built as an end-to-end system via a neural network. We take only text sequences, without using any syntactic parsing results or complex hand-designed features. We give public dataset [CoNLL-2004 and CoNLL-2005 Shared Tasks](http://www.cs.upc.edu/~srlconll/) as an example to illustrate: given a sentence and it's predicated, identify the corresponding arguments and their semantic roles by sequence tagging method.
In this tutorial, our SRL system is built as an end-to-end system via a neural network. We take only text sequences, without using any syntactic parsing results or complex hand-designed features. We give public dataset [CoNLL-2004 and CoNLL-2005 Shared Tasks](http://www.cs.upc.edu/~srlconll/) as an example to illustrate: given a sentence with predicates marked, identify the corresponding arguments and their semantic roles by sequence tagging method.
## Model
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@@ -92,7 +92,7 @@ Fig 4. Bidirectional LSTMs
线性变换-> linear transformation
输入层到隐层-> input-to-hidden
正向处理输出序列->process sequence in forwarding direction
正向处理输出序列->process sequence in the forward direction
反向处理上一层序列-> process sequence from the previous layer in backward direction
Note that, this bidirectional RNNs is different with the one proposed by Bengio et al. in machine translation tasks \[[3](#Reference), [4](#Reference)\]. We will introduce another bidirectional RNNs in the following tasks[machine translation](https://github.com/PaddlePaddle/book/blob/develop/machine_translation/README.md)
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@@ -148,9 +148,9 @@ We can try above method. Here, we propose some modifications by introducing two
After modification, the model is as follows:
1. Construct inputs
- Input 1: word sequence. Input 2: predicate. Input 3: predicate context, extract $n$ words before and after predicate. Input 4: region mark sequence, element value will be one if word locates in the predicate context region, 0 otherwise.
- Input 1: word sequence. Input 2: predicate. Input 3: predicate context, extract $n$ words before and after predicate. Input 4: region mark sequence, element value will be 1 if word locates in the predicate context region, 0 otherwise.
- expand input 2~3 as sequences with the same length with input 1
2. Convert input 1~4 to vector sequences via lookup table; input 1 and three shares the same lookup table, input 2 and 4 have separate lookup tables
2. Convert input 1~4 to vector sequences via lookup table; input 1 and 3 shares the same lookup table, input 2 and 4 have separate lookup tables
3. Take four vector sequences from step 2 as inputs of bidirectional LSTMs; Train LSTMs to update representations
4. Take representation from step 3 as input of CRF, label sequence as supervision signal, do sequence tagging tasks
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@@ -198,7 +198,7 @@ The raw data needs to be preprocessed before used by PaddlePaddle. The preproces
# conll05.test gets preprocessed training instances.
```
After preprocessing completes, a training sample contains nine features, namely: word sequence, predicate, predicate context (5 columns), region mark sequence, label sequence. Following table is an example of one training sample.
After preprocessing completes, a training sample contains nine features, namely: word sequence, predicate, predicate context (5 columns), region mark sequence, label sequence. Following table is an example of a training sample.
| word sequence | predicate | predicate context（5 columns） | region mark sequence | label sequence|
