yutanakamura-tky · iBotamon · Aug 11, 2019 · Aug 11, 2019 · Aug 11, 2019 · Aug 11, 2019
diff --git a/README.md b/README.md
@@ -9,27 +9,16 @@ CVPR, ICCV
 ### Natural Language Processing
 ACL, NAACL, EMNLP, CoNLL, COLING, IJCNLP, EACL, LREC, CL, SEMEVAL, TACL, ALTA, HLT, JEP-TALN-RECITAL, MUC, PACLIC, RANLP, ROCLING-IJCLCLP, TINLAP, TIPSTER
 
-# Usage
+# Usage(1): via command-line
+### Overview
 Run `medical_ai.py` on the shell:
 ```
-python3 medical_ai.py
-```
-
-Specify conference name and year:
-```
-Input conference name and year (e.g. 'naacl 2019') : 
-```
-
-Clipboard options:
-```
-Copy result on clipboard? (True/False) :
+python3 medical_ai.py <CONFERENCE> <YEAR>
 ```
 
 e.g. For `'nips 2018'`, you get 11 medical-like conference papers:
 ```
-python3 medical_ai.py 
-Input conference name and year (e.g. 'naacl 2019') : nips 2018
-Copy result on clipboard? (True/False) : False
+python3 medical_ai.py nips 2018
 Connecting...
 Searching... 11 matches / 1011
 ===================================
@@ -66,6 +55,120 @@ http://papers.nips.cc/paper/8125-bayesian-multi-domain-learning-for-cancer-subty
 Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.
 http://papers.nips.cc/paper/8193-life-long-disentangled-representation-learning-with-cross-domain-latent-homologies
 ===================================
-Medical-like AI papers: 11 / 1011
+Medical-like AI papers in NIPS 2018: 11 / 1011
+===================================
+```
+
+### Options
+
+
+Use `--html` option to display result as HTML link <a> tags:
+
+```
+python3 medical_ai.py nips 2018 --html
+Connecting...
+Searching... 11 match / 1011
+===================================
+<a href="http://papers.nips.cc/paper/7426-hybrid-retrieval-generation-reinforced-agent-for-medical-image-report-generation" target="_blank" alt="Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation.">Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation.</a><br/>
+<a href="http://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data" target="_blank" alt="Representation Learning for Treatment Effect Estimation from Observational Data.">Representation Learning for Treatment Effect Estimation from Observational Data.</a><br/>
+<a href="http://papers.nips.cc/paper/7702-lifelong-inverse-reinforcement-learning" target="_blank" alt="Lifelong Inverse Reinforcement Learning.">Lifelong Inverse Reinforcement Learning.</a><br/>
+<a href="http://papers.nips.cc/paper/7706-mime-multilevel-medical-embedding-of-electronic-health-records-for-predictive-healthcare" target="_blank" alt="MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare.">MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare.</a><br/>
+<a href="http://papers.nips.cc/paper/7817-mental-sampling-in-multimodal-representations" target="_blank" alt="Mental Sampling in Multimodal Representations.">Mental Sampling in Multimodal Representations.</a><br/>
+<a href="http://papers.nips.cc/paper/7962-refuel-exploring-sparse-features-in-deep-reinforcement-learning-for-fast-disease-diagnosis" target="_blank" alt="REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis.">REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis.</a><br/>
+<a href="http://papers.nips.cc/paper/7977-forecasting-treatment-responses-over-time-using-recurrent-marginal-structural-networks" target="_blank" alt="Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks.">Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks.</a><br/>
+<a href="http://papers.nips.cc/paper/8035-does-mitigating-mls-impact-disparity-require-treatment-disparity" target="_blank" alt="Does mitigating ML's impact disparity require treatment disparity?">Does mitigating ML's impact disparity require treatment disparity?</a><br/>
+<a href="http://papers.nips.cc/paper/8086-houdini-lifelong-learning-as-program-synthesis" target="_blank" alt="HOUDINI: Lifelong Learning as Program Synthesis.">HOUDINI: Lifelong Learning as Program Synthesis.</a><br/>
+<a href="http://papers.nips.cc/paper/8125-bayesian-multi-domain-learning-for-cancer-subtype-discovery-from-next-generation-sequencing-count-data" target="_blank" alt="Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data.">Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data.</a><br/>
+<a href="http://papers.nips.cc/paper/8193-life-long-disentangled-representation-learning-with-cross-domain-latent-homologies" target="_blank" alt="Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.">Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.</a>
+===================================
+Medical-like AI papers in NIPS 2018: 11 / 1011
+===================================
+```
+
+Use `--markdown` option to display result as markdown links:
+
+```
+python3 medical_ai.py nips 2018 --markdown
+Connecting...
+Searching... 11 match / 1011
+===================================
+[Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation.](http://papers.nips.cc/paper/7426-hybrid-retrieval-generation-reinforced-agent-for-medical-image-report-generation)
+[Representation Learning for Treatment Effect Estimation from Observational Data.](http://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data)
+[Lifelong Inverse Reinforcement Learning.](http://papers.nips.cc/paper/7702-lifelong-inverse-reinforcement-learning)
+[MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare.](http://papers.nips.cc/paper/7706-mime-multilevel-medical-embedding-of-electronic-health-records-for-predictive-healthcare)
+[Mental Sampling in Multimodal Representations.](http://papers.nips.cc/paper/7817-mental-sampling-in-multimodal-representations)
+[REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis.](http://papers.nips.cc/paper/7962-refuel-exploring-sparse-features-in-deep-reinforcement-learning-for-fast-disease-diagnosis)
+[Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks.](http://papers.nips.cc/paper/7977-forecasting-treatment-responses-over-time-using-recurrent-marginal-structural-networks)
+[Does mitigating ML's impact disparity require treatment disparity?](http://papers.nips.cc/paper/8035-does-mitigating-mls-impact-disparity-require-treatment-disparity)
+[HOUDINI: Lifelong Learning as Program Synthesis.](http://papers.nips.cc/paper/8086-houdini-lifelong-learning-as-program-synthesis)
+[Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data.](http://papers.nips.cc/paper/8125-bayesian-multi-domain-learning-for-cancer-subtype-discovery-from-next-generation-sequencing-count-data)
+[Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.](http://papers.nips.cc/paper/8193-life-long-disentangled-representation-learning-with-cross-domain-latent-homologies)
+===================================
+Medical-like AI papers in NIPS 2018: 11 / 1011
 ===================================
 ```
+
+Use `--copy` option to copy result onto clipboard:
+
+```
+python3 medical_ai.py nips 2018 --markdown --copy
+Connecting...
+Searching... 11 match / 1011
+===================================
+[Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation.](http://papers.nips.cc/paper/7426-hybrid-retrieval-generation-reinforced-agent-for-medical-image-report-generation)
+[Representation Learning for Treatment Effect Estimation from Observational Data.](http://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data)
+[Lifelong Inverse Reinforcement Learning.](http://papers.nips.cc/paper/7702-lifelong-inverse-reinforcement-learning)
+[MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare.](http://papers.nips.cc/paper/7706-mime-multilevel-medical-embedding-of-electronic-health-records-for-predictive-healthcare)
+[Mental Sampling in Multimodal Representations.](http://papers.nips.cc/paper/7817-mental-sampling-in-multimodal-representations)
+[REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis.](http://papers.nips.cc/paper/7962-refuel-exploring-sparse-features-in-deep-reinforcement-learning-for-fast-disease-diagnosis)
+[Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks.](http://papers.nips.cc/paper/7977-forecasting-treatment-responses-over-time-using-recurrent-marginal-structural-networks)
+[Does mitigating ML's impact disparity require treatment disparity?](http://papers.nips.cc/paper/8035-does-mitigating-mls-impact-disparity-require-treatment-disparity)
+[HOUDINI: Lifelong Learning as Program Synthesis.](http://papers.nips.cc/paper/8086-houdini-lifelong-learning-as-program-synthesis)
+[Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data.](http://papers.nips.cc/paper/8125-bayesian-multi-domain-learning-for-cancer-subtype-discovery-from-next-generation-sequencing-count-data)
+[Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.](http://papers.nips.cc/paper/8193-life-long-disentangled-representation-learning-with-cross-domain-latent-homologies)
+===================================
+Medical-like AI papers in NIPS 2018: 11 / 1011
+===================================
+ * * * Copied this result to clipboard * * *
+```
+
+
+To browse all available options, input `python3 medical_ai.py -h` or `python3 medical_ai.py --help`:
+
+
+```
+usage: medical_ai.py [-h] [-q] [--copy] [-m | --html]
+                     [--title-only | --url-only]
+                     conference year
+
+++++++++++++++++++++++++++++++++++++++++++++++++++
+Pickup medical AI paper titles and URLs from specified conference and year.
+会議名と年数を指定すると, 医療に関連するAI論文のみを探し出してタイトルとURLを列挙します.
+
+To get from ACL 2019, input like this: python3 medical_ai.py acl 2019
+例えばACL 2019採択論文から探すには本プログラムを python3 medical medical_ai.py acl 2019 と実行してください.
+
+Conference name is case insensitive.
+会議名は大文字でも小文字でも構いません.
+
+To output HTML link tags or markdown links, use options below.
+以下に示すオプションを使うと, 結果をHTMLリンクタグやMarkdownリンクとして出力することも可能です.
+++++++++++++++++++++++++++++++++++++++++++++++++++
+
+positional arguments:
+  conference            specify one conference (e.g. acl)
+  year                  specify one year (e.g. 2019)
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -q, --quiet           be more quiet
+  --copy                copy result to clipboard
+  -m, --md, --markdown  output as markdown links
+                        collaborates with --url-only
+                        ignores --title-only
+  --html                output as HTML <a> tags
+                        collaborates with --url-only
+                        ignores --title-only
+  --title-only          output paper title only
+  --url-only            output paper URL only
+```