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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.02289">arXiv:2205.02289</a> <span> [<a href="https://arxiv.org/pdf/2205.02289">pdf</a>, <a href="https://arxiv.org/format/2205.02289">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> A Dataset for N-ary Relation Extraction of Drug Combinations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tiktinsky%2C+A">Aryeh Tiktinsky</a>, <a href="/search/cs?searchtype=author&query=Viswanathan%2C+V">Vijay Viswanathan</a>, <a href="/search/cs?searchtype=author&query=Niezni%2C+D">Danna Niezni</a>, <a href="/search/cs?searchtype=author&query=Azagury%2C+D+M">Dana Meron Azagury</a>, <a href="/search/cs?searchtype=author&query=Shamay%2C+Y">Yosi Shamay</a>, <a href="/search/cs?searchtype=author&query=Taub-Tabib%2C+H">Hillel Taub-Tabib</a>, <a href="/search/cs?searchtype=author&query=Hope%2C+T">Tom Hope</a>, <a href="/search/cs?searchtype=author&query=Goldberg%2C+Y">Yoav Goldberg</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.02289v1-abstract-short" style="display: inline;"> Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available in a situation. To assist medical professionals in identifying beneficial drug-combinations, we construct an expert-annotated dataset for extr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.02289v1-abstract-full').style.display = 'inline'; document.getElementById('2205.02289v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.02289v1-abstract-full" style="display: none;"> Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available in a situation. To assist medical professionals in identifying beneficial drug-combinations, we construct an expert-annotated dataset for extracting information about the efficacy of drug combinations from the scientific literature. Beyond its practical utility, the dataset also presents a unique NLP challenge, as the first relation extraction dataset consisting of variable-length relations. Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task. We provide a promising baseline model and identify clear areas for further improvement. We release our dataset, code, and baseline models publicly to encourage the NLP community to participate in this task. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.02289v1-abstract-full').style.display = 'none'; document.getElementById('2205.02289v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear in NAACL 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.01306">arXiv:2005.01306</a> <span> [<a href="https://arxiv.org/pdf/2005.01306">pdf</a>, <a href="https://arxiv.org/format/2005.01306">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> pyBART: Evidence-based Syntactic Transformations for IE </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tiktinsky%2C+A">Aryeh Tiktinsky</a>, <a href="/search/cs?searchtype=author&query=Goldberg%2C+Y">Yoav Goldberg</a>, <a href="/search/cs?searchtype=author&query=Tsarfaty%2C+R">Reut Tsarfaty</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.01306v2-abstract-short" style="display: inline;"> Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These syntactic dependencies are designed to accurately reflect syntactic relations, and they do not make semantic relations explicit. Therefore, these representations lack many explicit connections between conte… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.01306v2-abstract-full').style.display = 'inline'; document.getElementById('2005.01306v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.01306v2-abstract-full" style="display: none;"> Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These syntactic dependencies are designed to accurately reflect syntactic relations, and they do not make semantic relations explicit. Therefore, these representations lack many explicit connections between content words, that would be useful for downstream applications. Proposals like English Enhanced UD improve the situation by extending universal dependency trees with additional explicit arcs. However, they are not available to Python users, and are also limited in coverage. We introduce a broad-coverage, data-driven and linguistically sound set of transformations, that makes event-structure and many lexical relations explicit. We present pyBART, an easy-to-use open-source Python library for converting English UD trees either to Enhanced UD graphs or to our representation. The library can work as a standalone package or be integrated within a spaCy NLP pipeline. When evaluated in a pattern-based relation extraction scenario, our representation results in higher extraction scores than Enhanced UD, while requiring fewer patterns. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.01306v2-abstract-full').style.display = 'none'; document.getElementById('2005.01306v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted ACL2020 system demonstration paper</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: 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