Resolving Citation Links With Neural Networks

This work demonstrates how neural network models (NNs) can be exploited toward resolving citation links in the scientific literature, which involves locating passages in the source paper the author had intended when citing the paper. We look at two kinds of models: triplet and binary. The triplet network model works by ranking potential candidates, using what is generally known as the triplet loss, while the binary model tackles the issue by turning it into a binary decision problem, i.e., by labeling a candidate as true or false, depending on how likely a target it is. Experiments are conducted using three datasets developed by the CL-SciSumm project from a large repository of scientific papers in the Association for Computational Linguistics (ACL) repository. The results find that NNs are extremely susceptible to how the input is represented: they perform better on inputs expressed in binary format than on those encoded using the TFIDF metric or neural embeddings of specific kinds. Furthermore, in response to a difficulty NNs and baselines faced in predicting the exact location of a target, we introduce the idea of approximately correct targets (ACTs) where the goal is to find a region which likely contains a true target rather than its exact location. We show that with the ACTs, NNs consistently outperform Ranking SVM and TFIDF on the aforementioned datasets

Does splitting make sentence easier?

In this study, we focus on sentence splitting, a subfield of text simplification, motivated largely by an unproven idea that if you divide a sentence in pieces, it should become easier to understand. Our primary goal in this study is to find out whether this is true. In particular, we ask, does it matter whether we break a sentence into two, three, or more? We report on our findings based on Amazon Mechanical Turk. More specifically, we introduce a Bayesian modeling framework to further investigate to what degree a particular way of splitting the complex sentence affects readability, along with a number of other parameters adopted from diverse perspectives, including clinical linguistics, and cognitive linguistics. The Bayesian modeling experiment provides clear evidence that bisecting the sentence leads to enhanced readability to a degree greater than when we create simplification with more splits

Lexico-syntactic Text Simplification And Compression With Typed Dependencies

We describe two systems for text simplification using typed dependency structures, one that performs lexical and syntactic simplification, and another that performs sentence compression optimised to satisfy global text constraints such as lexical density, the ratio of difficult words, and text length. We report a substantial evaluation that demonstrates the superiority of our systems, individually and in combination, over the state of the art, and also report a comprehension based evaluation of contemporary automatic text simplification systems with target non-native readers

Usefulness of Continuous Regional Arterial Infusion with Doripenem and Protease Inhibitors for Severe Acute Pancreatitis

Doripenem (DRPM) is a relatively new drug belonging to the carbapenem antibiotic group. We hypothesized that the pharmacological characteristics of DRPM could make it useful in the treatment of severe acute pancreatitis (SAP). We investigated the usefulness of continuous regional arterial infusion (CRAI) with DRPM and protease inhibitors for SAP. Two hundred and forty-two patients with SAP were admitted to Showa University Hospital between November 2002 and June 2013. Of these, 53 patients were treated with CRAI with carbapenem antibiotics and nafamostat mesilate (NM), a serine protease inhibitor, via the celiac and superior mesenteric arteries. Clinical outcomes were evaluated retrospectively in 34 patients treated with DRPM and 19 patients undergoing non-DRPM therapy (meropenem n=11, imipenem n=6; biapenem n=2). The median time to commencement of oral intake was significantly shorter in the DRPM than non-DRPM group (9 vs 14 hospital days, respectively; P<0.01). In addition, the rate of walled-off necrosis in the DRPM group tended to be lower than in the non-DRPM group (37.5 vs 64.7%, respectively, P=0.069). The results of the present study suggest that CRAI with DRPM and NM for SAP could have equivalent therapeutic effects to CRAI with other carbapenem antibiotics and NM

Extended Multilingual Protest News Detection -- Shared Task 1, CASE 2021 and 2022

We report results of the CASE 2022 Shared Task 1 on Multilingual Protest Event Detection. This task is a continuation of CASE 2021 that consists of four subtasks that are i) document classification, ii) sentence classification, iii) event sentence coreference identification, and iv) event extraction. The CASE 2022 extension consists of expanding the test data with more data in previously available languages, namely, English, Hindi, Portuguese, and Spanish, and adding new test data in Mandarin, Turkish, and Urdu for Sub-task 1, document classification. The training data from CASE 2021 in English, Portuguese and Spanish were utilized. Therefore, predicting document labels in Hindi, Mandarin, Turkish, and Urdu occurs in a zero-shot setting. The CASE 2022 workshop accepts reports on systems developed for predicting test data of CASE 2021 as well. We observe that the best systems submitted by CASE 2022 participants achieve between 79.71 and 84.06 F1-macro for new languages in a zero-shot setting. The winning approaches are mainly ensembling models and merging data in multiple languages. The best two submissions on CASE 2021 data outperform submissions from last year for Subtask 1 and Subtask 2 in all languages. Only the following scenarios were not outperformed by new submissions on CASE 2021: Subtask 3 Portuguese \& Subtask 4 English.Comment: To appear in CASE 2022 @ EMNLP 202