diff --git a/doc/howto/deep_model/rnn/rnn_en.rst b/doc/howto/deep_model/rnn/rnn_en.rst index 64f464b1dc0546462cd6b9294e93e5be935e4f46..b4c0c8bb4cf063872abc783932df737642fb9178 100644 --- a/doc/howto/deep_model/rnn/rnn_en.rst +++ b/doc/howto/deep_model/rnn/rnn_en.rst @@ -246,6 +246,6 @@ The code is listed below: outputs(beam_gen) -Notice that this generation technique is only useful for decoder like generation process. If you are working on sequence tagging tasks, please refer to :ref:`sentiment_analysis_en` for more details. +Notice that this generation technique is only useful for decoder like generation process. If you are working on sequence tagging tasks, please refer to :ref:`semantic_role_labeling_en` for more details. The full configuration file is located at :code:`demo/seqToseq/seqToseq_net.py`. diff --git a/doc/tutorials/semantic_role_labeling/index_en.md b/doc/tutorials/semantic_role_labeling/index_en.md index f5bdf64487aa189cefcd55d633cc6638912b9e31..bdd12c0d9abd759d8507a3029f373dc5db6f8f40 100644 --- a/doc/tutorials/semantic_role_labeling/index_en.md +++ b/doc/tutorials/semantic_role_labeling/index_en.md @@ -1,3 +1,7 @@ +```eval_rst +.. _semantic_role_labeling_en: +``` + # Semantic Role labeling Tutorial # Semantic role labeling (SRL) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence. SRL is useful as an intermediate step in a wide range of natural language processing tasks, such as information extraction. automatic document categorization and question answering. An instance is as following [1]: diff --git a/doc/tutorials/sentiment_analysis/index_en.md b/doc/tutorials/sentiment_analysis/index_en.md index 279ebddf19697d8646b72b87ec4f1d727452d4d1..bb7681db44ca6f286ad6935ddfecb9becb429192 100644 --- a/doc/tutorials/sentiment_analysis/index_en.md +++ b/doc/tutorials/sentiment_analysis/index_en.md @@ -1,7 +1,3 @@ -```eval_rst -.. _sentiment_analysis_en: -``` - # Sentiment Analysis Tutorial Sentiment analysis has many applications. A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence or feature/aspect level. One simple example is to classify the customer reviews in a shopping website, a tourism website, and group buying websites like Amazon, TaoBao, Tmall etc.