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Eleonora Bernasconi commited on Sep 15, 2023

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38d50e4

1 Parent(s): a67f2c7

doi from title

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__pycache__/scholarly.cpython-37.pyc +0 -0
app.py +6 -7
note.txt +2 -0
scholarly.py +13 -4

__pycache__/scholarly.cpython-37.pyc CHANGED Viewed

Binary files a/__pycache__/scholarly.cpython-37.pyc and b/__pycache__/scholarly.cpython-37.pyc differ

app.py CHANGED Viewed

@@ -15,7 +15,8 @@ st.write("Data from CSV:")
 st.write(data)
 cit_array = []
 if 'doi' not in data.columns:
     st.write("The 'doi' column does not exist in the CSV.")
 else:
@@ -23,23 +24,21 @@ else:
     for index, row in data.iterrows():
         doi = row['doi']
         if doi:
-            # st.text(f"1 Extracting DOI: {doi}")
             citation_count = scholarly.get_citation_count(doi)
             if citation_count != None:
                 cit_array.append(citation_count)
                 st.text(f"DOI: {doi}, Citation Count: {citation_count}")
             else:
                 # Handle cases where DOI is None (e.g., bytitle lookup)
                 title = row['title']
                 doi_bytitle = scholarly.get_doi_from_title(str(title))
-                # st.text(title)
-                # st.text(f"Extracting DOI from Title: {title}")
                 citation_count_title = scholarly.get_citation_count(doi_bytitle)
                 cit_array.append(citation_count_title)
                 st.text(f"DOI from Title: {title}, Citation Count: {citation_count_title}")
-                # else:
-            #     cit_array.append(None)
 # Add the citation count column to the DataFrame
 data['Citation Count'] = cit_array
 st.write(data)

 st.write(data)
 cit_array = []
+count = 0
+st.write(count)
 if 'doi' not in data.columns:
     st.write("The 'doi' column does not exist in the CSV.")
 else:
     for index, row in data.iterrows():
         doi = row['doi']
         if doi:
             citation_count = scholarly.get_citation_count(doi)
             if citation_count != None:
                 cit_array.append(citation_count)
                 st.text(f"DOI: {doi}, Citation Count: {citation_count}")
+                count += 1
             else:
                 # Handle cases where DOI is None (e.g., bytitle lookup)
                 title = row['title']
                 doi_bytitle = scholarly.get_doi_from_title(str(title))
                 citation_count_title = scholarly.get_citation_count(doi_bytitle)
+                if citation_count_title != None:
+                    count += 1
                 cit_array.append(citation_count_title)
                 st.text(f"DOI from Title: {title}, Citation Count: {citation_count_title}")
 # Add the citation count column to the DataFrame
 data['Citation Count'] = cit_array
 st.write(data)

note.txt ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ , 'unstructured': 'Goldberg Y (2017) Neural network methods in natural language processing. Morgan and Claypool Publishers', 'DOI': '10.1007/978-3-031-02165-7'}, {'key': '9301_CR20', 'unstructured': 'Goodfellow I, Y YB, Courville A, (2016) Deep learning. MIT Press, Cambridge'}, {'issue': '4', 'key': '9301_CR21', 'doi-asserted-by': 'publisher', 'first-page': '415', 'DOI': '10.1007/s10506-020-09260-6', 'volume': '28', 'author': 'P Hacker', 'year': '2020', 'unstructured': 'Hacker P, Krestel R, Grundmann S, Naumann F (2020) Explainable AI under contract and tort law: legal incentives and technical challenges. Artif Intell Law 28(4):415–439', 'journal-title': 'Artif Intell Law'}, {'key': '9301_CR22', 'unstructured': 'Hu Z, Li X, Tu C, Liu Z, Sun M (2018) Few-shot charge prediction with discriminative legal attributes. In: Proceedings of COLING, pp 487–498. Association for Computational Linguistics'}, {'key': '9301_CR23', 'unstructured': 'Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall'}, {'issue': '5', 'key': '9301_CR24', 'doi-asserted-by': 'publisher', 'first-page': '493', 'DOI': '10.1108/00220410410560573', 'volume': '60', 'author': 'KS Jones', 'year': '2004', 'unstructured': 'Jones KS (2004) A statistical interpretation of term specificity and its application in retrieval. J Documentation 60(5):493–502', 'journal-title': 'J Documentation'}, {'key': '9301_CR26', 'doi-asserted-by': 'crossref', 'unstructured': 'Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of EMNLP, pp 1746–1751', 'DOI': '10.3115/v1/D14-1181'}, {'key': '9301_CR25', 'unstructured': 'Kim M, Xu Y, Goebel R (2015) A convolutional neural network in legal question answering. In: Proc int workshop on juris-informatics JURISIN'}, {'key': '9301_CR27', 'doi-asserted-by': 'crossref', 'unstructured': 'Lai S, Xu L, Liu K, Zhao J (2015) Recurrent convolutional neural networks for text classification. In: Proceedings of AAAI, pp 2267–2273', 'DOI': '10.1609/aaai.v29i1.9513'}, {'key': '9301_CR28', 'doi-asserted-by': 'publisher', 'first-page': '151144', 'DOI': '10.1109/ACCESS.2019.2945771', 'volume': '7', 'author': 'S Li', 'year': '2019', 'unstructured': 'Li S, Zhang H, Ye L, Guo X, Fang B (2019) MANN: a multichannel attentive neural network for legal judgment prediction. IEEE Access 7:151144–151155', 'journal-title': 'IEEE Access'}, {'key': '9301_CR29', 'unstructured': 'Lin W, Kuo T, Chang T, Yen C, Chen C, Lin S (2012) Exploiting machine learning models for Chinese legal documents labeling, case classification, and sentencing prediction. IJCLCLP 17(4)'}, {'key': '9301_CR30', 'doi-asserted-by': 'crossref', 'unstructured': 'Liu C, Hsieh C (2006) Exploring phrase-based classification of judgment documents for criminal charges in Chinese. In: Proceedings of ISMIS, pp 681–690', 'DOI': '10.1007/11875604_75'}, {'issue': '1', 'key': '9301_CR32', 'doi-asserted-by': 'publisher', 'first-page': '194', 'DOI': '10.1016/j.ipm.2014.07.003', 'volume': '51', 'author': 'YH Liu', 'year': '2015', 'unstructured': 'Liu YH, Chen YL, Ho WL (2015) Predicting associated statutes for legal problems. Inf Proces Manag 51(1):194–211', 'journal-title': 'Inf Proces Manag'}, {'key': '9301_CR31', 'unstructured': 'Liu P, Qiu X, Huang X (2016) Recurrent neural network for text classification with multi-task learning. In: Proceedings of IJCAI, pp 2873–2879'}, {'key': '9301_CR33', 'doi-asserted-by': 'crossref', 'unstructured': 'Long S, Tu C, Liu Z, Sun M (2019) Automatic judgment prediction via legal reading comprehension. 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In: Proceedings of ICAL workshops', 'DOI': '10.1007/s10506-018-9225-1'}, {'issue': '2', 'key': '9301_CR42', 'doi-asserted-by': 'publisher', 'first-page': '169', 'DOI': '10.1007/s10506-018-9225-1', 'volume': '26', 'author': 'T Nguyen', 'year': '2018', 'unstructured': 'Nguyen T, Nguyen L, Tojo S, Satoh K, Shimazu A (2018) Recurrent neural network-based models for recognizing requisite and effectuation parts in legal texts. Artif Intell Law 26(2):169–199', 'journal-title': 'Artif Intell Law'}, {'key': '9301_CR40', 'unstructured': 'Nguyen H, Nguyen PM, Vuong T, Bui QM, Nguyen CM, Dang TB, Tran V, Nguyen ML, Satoh K (2021) JNLP team: Deep learning approaches for legal processing tasks in COLIEE 2021. CoRR arXiv:2106.13405'}, {'key': '9301_CR43', 'doi-asserted-by': 'crossref', 'unstructured': 'O’Neill J, Buitelaar P, Robin C, O’Brien L (2017) Classifying sentential modality in legal language: a use case in financial regulations, acts and directives. In: Proceedings of international conference on artificial intelligence and law (ICAIL), pp 159–168', 'DOI': '10.1145/3086512.3086528'}, {'key': '9301_CR44', 'doi-asserted-by': 'crossref', 'unstructured': 'Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. In: Proceedings of NAACL-HLT, pp 2227–2237', 'DOI': '10.18653/v1/N18-1202'}, {'key': '9301_CR45', 'unstructured': 'Polignano M, Basile P, de\xa0Gemmis M, Semeraro G, Basile V (2019) AlBERTo: Italian BERT language understanding model for NLP challenging tasks based on tweets. In: Proceedings of 6th Italian conference on computational linguistics. CEUR workshop proceedings, vol 2481. http://ceur-ws.org/'}, {'key': '9301_CR46', 'unstructured': 'Puccinelli D, Demartini S, D’Aoust RE (2019) Fixing comma splices in Italian with BERT. In: Proceedings of 6th Italian conference on computational linguistics. 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RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper, we evaluate the impact of joint training of the retriever and generator components of RAG for the task of domain adaptation in ODQA. We propose RAG-end2end, an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. In addition, we introduce an auxiliary training signal to inject more domain-specific knowledge. This auxiliary signal forces RAG-end2end to reconstruct a given sentence by accessing the relevant information from the external knowledge base. Our novel contribution is that, unlike RAG, RAG-end2end does joint training of the retriever and generator for the end QA task and domain adaptation. We evaluate our approach with datasets from three domains: COVID-19, News, and Conversations, and achieve significant performance improvements compared to the original RAG model. Our work has been open-sourced through the HuggingFace Transformers library, attesting to our work’s credibility and technical consistency.</jats:p>', 'DOI': '10.1162/tacl_a_00530', 'type': 'journal-article', 'created': {'date-parts': [[2023, 1, 24]], 'date-time': '2023-01-24T16:17:15Z', 'timestamp': 1674577035000}, 'page': '1-17', 'update-policy': 'http://dx.doi.org/10.1162/mitpressjournals.corrections.policy', 'source': 'Crossref', 'is-referenced-by-count': 3, 'title': ['Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering'], 'prefix': '10.1162', 'volume': '11', 'author': [{'given': 'Shamane', 'family': 'Siriwardhana', 'sequence': 'first', 'affiliation': [{'name': 'Augmented Human Lab, Auckland Bioengineering Institute, The University of Auckland, New Zealand. 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2	+ {'status': 'ok', 'message-type': 'work-list', 'message-version': '1.0.0', 'message': {'facets': {}, 'total-results': 498790, 'items': [{'indexed': {'date-par

scholarly.py CHANGED Viewed

@@ -1,7 +1,7 @@
 from habanero import counts
 from habanero import Crossref
 def get_citation_count(doi):
     try:
@@ -15,6 +15,15 @@ def get_doi_from_title(title):
     try:
         cr = Crossref()
         result = cr.works(query = title)
-        return (result['message']['items'][0]['DOI'])
-    except:
         return None

 from habanero import counts
 from habanero import Crossref
+import pdb
+from fuzzywuzzy import fuzz
 def get_citation_count(doi):
     try:
     try:
         cr = Crossref()
         result = cr.works(query = title)
+        for item in result['message']['items']:
+            retrieved_title = item.get('title', [''])[0].lower()  # Convert retrieved title to lowercase
+            similarity_score = fuzz.ratio(title.lower(), retrieved_title) / 100  # Calcola il punteggio di similarità
+            # soglia di similarità desiderata (75%)
+            similarity_threshold = 0.75
+            print(retrieved_title, similarity_score)
+            if similarity_score >= similarity_threshold:
+                # pdb.set_trace()
+                return item.get('DOI', None)
+    except Exception as e:
         return None