Patent Claim Decomposition for Improved Information Extraction

Parapatics, Peter; Dittenbach, Michael

doi:10.1007/978-3-642-19231-9_10

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…While it has been argued that patent searching must become part of life science students’ information literacy instruction, 12 a more pragmatic and encompassing approach could be algorithmically mapping patents to MeSH codes, 13 which would allow integrated searches that cover both PubMed and open access patent databases. Although algorithmic parsing of typical “patent jargon” into semantics that are familiar to research scientists is only a dimly perceived possibility at this time, 14 text analysis has been able for some years to effectively identify innovative patents and provide ranking and mapping. 15 For specialized applications such as the vascular risk management literature, expert systems might provide broadly deployable semi-automated solutions within the next few years.…”

Section: Discussionmentioning

confidence: 99%

Relating patenting and peer-review publications: an extended perspective on the vascular health and risk management literature

Mucke¹

2011

VHRM

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Purpose:This investigation identifies patent applications published under the international Patent Convention Treaty between July 2010 and January 2011 in three significant fields of vascular risk management (arterial hypertension, atherosclerosis, and aneurysms) and investigates whether the inventors have also published peer reviewed papers directly describing their claimed invention.Results:Out of only 48 patent documents that specifically addressed at least one of the above-mentioned fields, 15 had immediate companion papers of which 13 were published earlier than the corresponding patent applications; the majority of these papers were published by noncorporate patentees. Although the majority of patent applications (30 documents) had at least one corporate assignee, 18 came from academic environments. As expected, medical devices dominated in the aneurysm segment while pharmacology dominated hypertension and atherosclerosis.Conclusion:Although information related to hypertension, atherosclerosis, or aneurysms that was claimed in international patent applications reached the public quicker through the corresponding peer review document if one was published, more than two-thirds of the patent applications had no such companion paper in a scientific journal. The patent literature, which is freely available online as full text, offers information to scientists and developers in the fields of vascular risk management that is not available from the peer reviewed literature.

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Section: Discussionmentioning

confidence: 99%

Relating patenting and peer-review publications: an extended perspective on the vascular health and risk management literature

Mucke¹

2011

VHRM

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“…The parser has not been optimized/retrained for the patent domain. 17 In spite of the technical difficulties (Parapatics and Dittenbach 2009) and loss of linguistic accuracy for patent texts reported in Mille and Wanner (2008), most patent processing systems that use linguistic phrases use the Stanford parser because its dependency scheme has a number of properties that are valuable for Text Mining purposes (de Marneffe and Manning 2008). The Stanford parser collapsed typed dependency model has a set of 55 different syntactic relators to capture semantically contentful relations between words.…”

Section: Stanfordmentioning

confidence: 99%

Text Representations for Patent Classification

D’Hondt

Verberne

Koster

et al. 2013

Computational Linguistics

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With the increasing rate of patent application filings, automated patent classification is of rising economic importance. This article investigates how patent classification can be improved by using different representations of the patent documents. Using the Linguistic Classification System (LCS), we compare the impact of adding statistical phrases (in the form of bigrams) and linguistic phrases (in two different dependency formats) to the standard bag-of-words text representation on a subset of 532,264 English abstracts from the CLEF-IP 2010 corpus.In contrast to previous findings on classification with phrases in the Reuters-21578 data set, for patent classification the addition of phrases results in significant improvements over the unigram baseline. The best results were achieved by combining all four representations, and the second best by combining unigrams and lemmatized bigrams. This article includes extensive analyses of the class models (a.k.a. class profiles) created by the classifiers in the LCS framework, to examine which types of phrases are most informative for patent classification. It appears that bigrams contribute most to improvements in classification accuracy. Similar experiments were performed on subsets of French and German abstracts to investigate the generalizability of these findings.

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“…Due to the significant value of information in chemical patents, many research efforts have been made toward the development of more effective information extraction systems specifically for chemical patents (Parapatics and Dittenbach, 2011 ; Akhondi et al, 2014 ; Chen et al, 2020 ). Several fundamental information extraction tasks, such as named entity recognition (NER) (Zhai et al, 2019 ), and relation extraction (Peng et al, 2018 ) have been extensively investigated.…”

Section: Introductionmentioning

confidence: 99%

ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents

Nguyen

Akhondi

et al. 2021

Front. Res. Metr. Anal.

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Chemical patents represent a valuable source of information about new chemical compounds, which is critical to the drug discovery process. Automated information extraction over chemical patents is, however, a challenging task due to the large volume of existing patents and the complex linguistic properties of chemical patents. The Cheminformatics Elsevier Melbourne University (ChEMU) evaluation lab 2020, part of the Conference and Labs of the Evaluation Forum 2020 (CLEF2020), was introduced to support the development of advanced text mining techniques for chemical patents. The ChEMU 2020 lab proposed two fundamental information extraction tasks focusing on chemical reaction processes described in chemical patents: (1) chemical named entity recognition, requiring identification of essential chemical entities and their roles in chemical reactions, as well as reaction conditions; and (2) event extraction, which aims at identification of event steps relating the entities involved in chemical reactions. The ChEMU 2020 lab received 37 team registrations and 46 runs. Overall, the performance of submissions for these tasks exceeded our expectations, with the top systems outperforming strong baselines. We further show the methods to be robust to variations in sampling of the test data. We provide a detailed overview of the ChEMU 2020 corpus and its annotation, showing that inter-annotator agreement is very strong. We also present the methods adopted by participants, provide a detailed analysis of their performance, and carefully consider the potential impact of data leakage on interpretation of the results. The ChEMU 2020 Lab has shown the viability of automated methods to support information extraction of key information in chemical patents.

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Patent Claim Decomposition for Improved Information Extraction

Cited by 7 publications

References 4 publications

Relating patenting and peer-review publications: an extended perspective on the vascular health and risk management literature

Relating patenting and peer-review publications: an extended perspective on the vascular health and risk management literature

Text Representations for Patent Classification

ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents

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