bluuebunny/crossref_metadata_2025 huggingface dataset:
Uremic Toxin Indoxyl Sulfate Promotes Proinflammatory Macrophage Activation Via the Interplay of OATP2B1 and Dll4-Notch Signaling
Authors: Nakano Toshiaki , Katsuki Shunsuke , Chen Mingxian , Decano Julius L. , Halu Arda , Lee Lang Ho , Pestana Diego V. S. , Kum Angelo S. T. , Kuromoto Rodrigo K. , Golden Whitney S. , Boff Mario S. , Guimaraes Gabriel C. , Higashi Hideyuki , Kauffman Kevin J. , Maejima Takashi , Suzuki Takehiro , Iwata Hiroshi , Barabási Albert-László , Aster Jon C. , Anderson Daniel G. , Sharma Amitabh , Singh Sasha A. , Aikawa Elena , Aikawa Masanori
Published: December 2018

Background:

Chronic kidney disease (CKD) increases cardiovascular risk. Underlying mechanisms, however, remain obscure. The uremic toxin indoxyl sulfate is an independent cardiovascular risk factor in CKD. We explored the potential impact of indoxyl sulfate on proinflammatory activation of macrophages and its underlying mechanisms.

Methods:

We examined in vitro the effects of clinically relevant concentrations of indoxyl sulfate on proinflammatory responses of macrophages and the roles of organic anion transporters and organic anion transporting polypeptides (OATPs). A systems approach, involving unbiased global proteomics, bioinformatics, and network analysis, then explored potential key pathways. To address the role of Delta-like 4 (Dll4) in indoxyl sulfate–induced macrophage activation and atherogenesis in CKD in vivo, we used 5/6 nephrectomy and Dll4 antibody in low-density lipoprotein receptor–deficient (Ldlr-/-) mice. To further determine the relative contribution of OATP2B1 or Dll4 to proinflammatory activation of macrophages and atherogenesis in vivo, we used siRNA delivered by macrophage-targeted lipid nanoparticles in mice.

Results:

We found that indoxyl sulfate–induced proinflammatory macrophage activation is mediated by its uptake through transporters, including OATP2B1, encoded by the SLCO2B1 gene. The global proteomics identified potential mechanisms, including Notch signaling and the ubiquitin-proteasome pathway, that mediate indoxyl sulfate-triggered proinflammatory macrophage activation. We chose the Notch pathway as an example of key candidates for validation of our target discovery platform and for further mechanistic studies. As predicted computationally, indoxyl sulfate triggered Notch signaling, which was preceded by the rapid induction of Dll4 protein. Dll4 induction may result from inhibition of the ubiquitin-proteasome pathway, via the deubiquitinating enzyme USP5. In mice, macrophage-targeted OATP2B1/Slco2b1 silencing and Dll4 antibody inhibited proinflammatory activation of peritoneal macrophages induced by indoxyl sulfate. In low-density lipoprotein receptor–deficient mice, Dll4 antibody abolished atherosclerotic lesion development accelerated in Ldlr-/- mice. Moreover, coadministration of indoxyl sulfate and OATP2B1/Slco2b1 or Dll4 siRNA encapsulated in macrophage-targeted lipid nanoparticles in Ldlr-/- mice suppressed lesion development.

Conclusions:

These results suggest that novel crosstalk between OATP2B1 and Dll4-Notch signaling in macrophages mediates indoxyl sulfate–induced vascular inflammation in CKD.
Sustainability of Blood Pressure Reduction in Black Barbershops
Authors: Victor Ronald G. , Blyler Ciantel A. , Li Ning , Lynch Kathleen , Moy Norma B. , Rashid Mohamad , Chang L. Cindy , Handler Joel , Brettler Jeffrey , Rader Florian , Elashoff Robert M.
Published: December 2018

Background:

We developed a new model of hypertension care for non-Hispanic black men that links health promotion by barbers to medication management by American Society of Hypertension–certified pharmacists and demonstrated efficacy in a 6-month cluster-randomized trial. The marked reduction in systolic blood pressure (BP) seen at 6 months warranted continuing the trial through 12 months to test sustainability, a necessary precondition for implementation research.

Methods:

We enrolled a cohort of 319 black male patrons with systolic BP ≥140 mm Hg at baseline. Fifty-two Los Angeles County barbershops were assigned to either a pharmacist-led intervention or an active control group. In the intervention group, barbers promoted follow-up with pharmacists who prescribed BP medication under a collaborative practice agreement with patrons’ primary care providers. In the control group, barbers promoted follow-up with primary care providers and lifestyle modification. After BP assessment at 6 months, the intervention continued with fewer in-person pharmacist visits to test whether the intervention effect could be sustained safely for 1 year while reducing pharmacist travel time. Final BP and safety outcomes were assessed in both groups at 12 months.

Results:

At baseline, mean systolic BP was 152.4 mm Hg in the intervention group and 154.6 mm Hg in the control group. At 12 months, mean systolic BP fell by 28.6 mm Hg (to 123.8 mm Hg) in the intervention group and by 7.2 mm Hg (to 147.4 mm Hg) in the control group. The mean reduction was 20.8 mm Hg greater in the intervention (95% CI, 13.9–27.7;
P
<0.0001). A BP <130/80 mm Hg was achieved by 68.0% of the intervention group versus 11.0% of the control group (
P
<0.02). These new 12-month efficacy data are statistically indistinguishable from our previously reported 6-month data. No treatment-related serious adverse events occurred in either group over 12 months. Cohort retention at 12 months was 90% in both groups.

Conclusions:

Among black male barbershop patrons with uncontrolled hypertension, health promotion by barbers resulted in large and sustained BP reduction over 12 months when coupled with medication management by American Society of Hypertension–certified pharmacists. Broad-scale implementation research is both justified and warranted.

Clinical Trial Registration:

URL:
https://www.clinicaltrials.gov
. Unique identifier: NCT 02321618.
Favorable Cardiovascular Health at Young and Middle Ages and Dementia in Older Age—The CHA Study
Authors: Vu Thanh‐Huyen T. , Zhao Lihui , Liu Lei , Schiman Cuiping , Lloyd‐Jones Donald M. , Daviglus Martha L. , Liu Kiang , Garside Daniel B. , Stamler Jeremiah , Fries James F. , Shih Ya‐Chen Tina , Allen Norrina B.
Published: December 2018

Background

Data are sparse on the association of cardiovascular health (
CVH
) in younger/middle age with the incidence of dementia later in life.

Methods and Results

We linked the CHA (Chicago Heart Association Detection Project in Industry) study data, assessed in 1967 to 1973, with 1991 to 2010 Medicare and National Death Index data. Favorable
CVH
was defined as untreated systolic blood pressure/diastolic blood pressure ≤120/≤80 mm Hg, untreated serum total cholesterol <5.18 mmol/L, not smoking, bone mass index <25 kg/m
2
, and no diabetes mellitus.
International Classification of Diseases, Ninth Revision (ICD‐9)
codes and claims dates were used to identify the first dementia diagnosis. Cox models were used to estimate hazard ratios of incident dementia after age 65 years by baseline
CVH
status. Among 10 119 participants baseline aged 23 to 47 years, 32.4% were women, 9.2% were black, and 7.3% had favorable baseline
CVH
. The incidence rate of dementia during follow‐up after age 65 was 13.9%. After adjustment, the hazard ratio for incident dementia was lowest in those with favorable baseline
CVH
and increased with higher risk factor burden (
P
‐trend<0.001). The hazards of dementia in those with baseline favorable, moderate, and 1‐only high‐risk factor were lower by 31%, 26%, and 20%, respectively, compared with those with ≥2 high‐risk factors. The association was attenuated but remained significant (
P
‐trend<0.01) when the model was further adjusted for competing risk of death. Patterns of associations were similar for men and women, and for those with a higher and lower baseline education level.

Conclusions

In this large population‐based study, a favorable
CVH
profile at younger age is associated with a lower risk of dementia in older age.
Discussing Out‐of‐Pocket Costs With Patients: Shared Decision Making for Sacubitril‐Valsartan in Heart Failure
Authors: Smith Graham H. , Shore Supriya , Allen Larry A. , Markham David W. , Mitchell Andrea R. , Moore Miranda , Morris Alanna A. , Speight Candace D. , Dickert Neal W.
Published: December 2018

Background

“Financial toxicity” is a concern for patients, but little is known about how patients consider out‐of‐pocket cost in decisions. Sacubitril‐valsartan provides a contemporary scenario to understand financial toxicity. It is guideline recommended for heart failure with reduced ejection fraction, yet out‐of‐pocket costs can be considerable.

Methods and Results

Structured interviews were conducted with 49 patients with heart failure with reduced ejection fraction at heart failure clinics and inpatient services. Patient opinions of the drug and its value were solicited after description of benefits using graphical displays. Descriptive quantitative analysis of closed‐ended responses was conducted, and qualitative descriptive analysis of text data was performed. Of participants, 92% (45/49) said that they would definitely or probably switch to sacubitril‐valsartan if their physician recommended it and out‐of‐pocket cost was $5 more per month than their current medication. Only 43% (21/49) would do so if out‐of‐pocket cost was $100 more per month (
P
<0.001). At least 40% across all income categories would be unlikely to take sacubitril‐valsartan at $100 more per month. Participants exhibited heterogeneous approaches to cost in decision making and varied on their use and interpretation of probabilistic information. Few (20%) participants stated physicians had initiated a conversation about cost in the past year.

Conclusions

Out‐of‐pocket cost variation reflective of contemporary cost sharing substantially influenced stated willingness to take sacubitril‐valsartan, a guideline‐recommended therapy with mortality benefit. These findings suggest a need for cost transparency to promote shared decision making. They also demonstrate the complexity of cost discussion and need to study how to incorporate out‐of‐pocket cost into clinical decisions.
THE USE OF IMPORTANCE- PERFORMANCE ANALYSIS (IPA) IN EVALUATING BOZCAADA DESTINATION’S SERVICE QUALITY
Authors: DİKER Oğuz , YILDIRIM Hacı Mehmet , AKSU Murat
Published: December 2018

The purpose of this study is the importance of significance-performance
analysis in determining the service quality in destinations. Research was
conducted regarding domestic tourists in the destination of Bozcaada in Turkey. The survey was conducted on the related sample group via the
SPA scale, which was tested in terms of reliability and validity, and used in a
similar study. In Bozcaada, while (I) informing, (T) transportation, and (S) shopping factors were
given high averages of significance by the visitors, they were placed in the “concentrate
here” strategy zone since their performance averages were low. On the other
hand (A) accommodation, and (H) health and hygiene were positioned on “possible
overkill” strategy zone since they had lower significance averages. Elements such as time and cost are among the main limitations of this
study. Because of Bozcaada’s geographic features, it is difficult to generalize
the significance- performance analyses in this destination. That is why
re-application of the study in different seasons, on visitors with different
nationalities, and a comparison of the results will be important for a better understanding
of the elements that affect the service quality perception. The data obtained
from the research will be important regarding the elements that tourists, who
are visiting Bozcaada, attach importance to and affect from the performance. On
the other hand, the obtained data will reveal the current situation about the perception
of tourists pertaining to the service presented in Bozcaada, and thus enable the further evaluation of which strategies to focus
on.
TAKSİT ERTELEMELİ KREDİLERİN MUHASEBELEŞTİRİLMESİ: DEĞERLEME VE KAYITLAMA YAKLAŞIMLARINA İLİŞKİN ÖNERİLER
Authors: KÜÇÜK Ergün
Published: December 2018

Bu çalışmanın amacı; taksit ertelemeli kredilerin ilgili muhasebe
standartları çerçevesinde nasıl muhasebeleştirilmesi gerektiğini
değerlendirmektir. Bu amaçla, çalışmada; muhasebe dönemi içerisinde temin
edilen taksit ertelemeli bir kredinin doğurmuş olduğu maliyet (faiz) yükünün itfa
edilmiş maliyet yöntemine uygun olarak birden fazla muhasebe dönemiyle nasıl ilişkilendirileceği
ve finansal raporlamasının nasıl yapılacağı üzerinde durulmuştur. Ayrıca çalışmada
konu kapsamına giren finansal olaylarda kullanımı söz konucu olabilecek iki
farklı hesaplama yaklaşımına ek olarak, iki farklı kayıtlama tarzı önerisinde
bulunulmuştur.
YÖRESEL MUTFAK MOTİVASYON UNSURLARININ DESTİNASYONU TEKRAR TERCİH ETME VE BAŞKALARINA ÖNERME ÜZERİNE ETKİSİ
Authors: ŞENGÜL Serkan , TÜRKAY Oğuz
Published: December 2018

Turistlerin yöresel mutfak tercihindeki motivasyon unsurlarının destinasyonu tekrar tercih etme ve başkalarına önerme üzerindeki etkilerinin incelenmesi amacıyla tasarlanan bu çalışma, yöresel mutfak tercihindeki motivasyon unsurlarının, destinasyon yöresel mutfak memnuniyeti ve satınalma sonrası davranışlarla arasındaki ilişkiyi göstermesi açısından önemlidir. Sakarya il merkezi ve ilçelerini ziyaret eden 662 turistten veri elde edilmiştir. Yapısal Eşitlik Modellemesi (YEM) ile test edilen modelin kabul edilebilir olduğu görülmüştür. Araştırmada elde edilen sonuçlara göre yöresel mutfak motivasyon unsurları içerisinde yer alan “duyulara hitap”, “heyecan verici deneyim”, “rutinden kaçış”, “sağlık endişesi”, “birliktelik” faktörlerinin “yöresel mutfak memnuniyeti”ni anlamlı düzeyde etkilediği, “kültürel deneyim”, “ekonomik faktörler” ve “prestij” faktörlerinin ise anlamlı etki üretmediği tespit edilmiştir. “Yöresel mutfak memnuniyeti” faktörünün ise “yöresel mutfaklar için destinasyonu tekrar tercih etme ve başkalarına önerme” faktörlerini anlamlı düzeyde açıkladığı sonucuna ulaşılmıştır.
CURRENT ACCOUNT IMBALANCE AND FOREIGN CAPITAL INFLOWS IN TURKEY: WHAT CAUSES WHAT?
Authors: Barış Tüzemen Özge , Tüzemen Samet
Published: December 2018

Turkish
economy suffers from chronic current account deficit for many years due to insufficient
savings and dependency on import energy and intermediate goods. In order to close
the deficit, many developing countries need foreign capital. This is also the
case for the Turkish economy. In the pre-financial liberalization period, the
direction of causality would be expected from current account balance to
foreign capital. After the liberalization, on the other hand, the direction of
causality has become an intensively debated issue. This study aims to determine
the direction of the causality between current account and financial account
using Toda-Yamamoto causality and Hatemi-J asymmetric causality tests. The data
cover the period of 2002:01-2017:12. Evidence from the asymmetric causality
test shows that the negative shocks of the portfolio investment Granger causes
both the negative and the positive shocks of the current account balance in
Turkey.
THE PORTFOLIO MANAGEMENT WITH ISLAM EQUITY IN KOREA STOCK MARKET
Authors: KİM Hongbae , SOHN Taewoo , YOUN Heejung
Published: December 2018

This
paper investigated the volatility spillover effects between Islamic stock
markets and Korean stock market using the AR-DCC-GARCH models. First, we found
bi-directional volatility transmissions between the Islamic and Korean
financial markets. Second, we compared the correlation of KOSPI-DJIM portfolio
and that of KOSPI-SHX portfolio. It shows the former has stronger linkage than
the latter. In the portfolio perspective, the S&P 500 Sharia stock Index (SHX)
acts as a better hedge asset than DJIM against the risk of stock market. Lastly,
the hedge ratio between two Islamic stock market and Korean stock market pairs
is generally low, indicating that the Korean stock risk can be effectively
hedged by taking a short position in the Islamic stock markets. In comparison
with two pairs, the pair of KOSPI-SHX relatively shows a cheaper hedging cost
than that of KOSP-DJIM pair. This evidence indicates that S&P 500 Sharia
index serve more effective hedging role against the risk of Korean stock
market.
Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Topic Labels
Authors: Smith Alison , Lee Tak Yeon , Poursabzi-Sangdeh Forough , Boyd-Graber Jordan , Elmqvist Niklas , Findlater Leah
Published: December 2018

Probabilistic topic models are important tools for indexing, summarizing, and analyzing large document collections by their themes. However, promoting end-user understanding of topics remains an open research problem. We compare labels generated by users given four topic visualization techniques—word lists, word lists with bars, word clouds, and network graphs—against each other and against automatically generated labels. Our basis of comparison is participant ratings of how well labels describe documents from the topic. Our study has two phases: a labeling phase where participants label visualized topics and a validation phase where different participants select which labels best describe the topics’ documents. Although all visualizations produce similar quality labels, simple visualizations such as word lists allow participants to quickly understand topics, while complex visualizations take longer but expose multi-word expressions that simpler visualizations obscure. Automatic labels lag behind user-created labels, but our dataset of manually labeled topics highlights linguistic patterns (e.g., hypernyms, phrases) that can be used to improve automatic topic labeling algorithms.
Enriching Word Vectors with Subword Information
Authors: Bojanowski Piotr , Grave Edouard , Joulin Armand , Mikolov Tomas
Published: December 2018

Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows us to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.
Unsupervised Learning of Morphological Forests
Authors: Luo Jiaming , Narasimhan Karthik , Barzilay Regina
Published: December 2018

This paper focuses on unsupervised modeling of morphological families, collectively comprising a forest over the language vocabulary. This formulation enables us to capture edge-wise properties reflecting single-step morphological derivations, along with global distributional properties of the entire forest. These global properties constrain the size of the affix set and encourage formation of tight morphological families. The resulting objective is solved using Integer Linear Programming (ILP) paired with contrastive estimation. We train the model by alternating between optimizing the local log-linear model and the global ILP objective. We evaluate our system on three tasks: root detection, clustering of morphological families, and segmentation. Our experiments demonstrate that our model yields consistent gains in all three tasks compared with the best published results.
Fully Character-Level Neural Machine Translation without Explicit Segmentation
Authors: Lee Jason , Cho Kyunghyun , Hofmann Thomas
Published: December 2018

Most existing machine translation systems operate at the level of words, relying on explicit segmentation to extract tokens. We introduce a neural machine translation (NMT) model that maps a source character sequence to a target character sequence without any segmentation. We employ a character-level convolutional network with max-pooling at the encoder to reduce the length of source representation, allowing the model to be trained at a speed comparable to subword-level models while capturing local regularities. Our character-to-character model outperforms a recently proposed baseline with a subword-level encoder on WMT’15 DE-EN and CS-EN, and gives comparable performance on FI-EN and RU-EN. We then demonstrate that it is possible to share a single character-level encoder across multiple languages by training a model on a many-to-one translation task. In this multilingual setting, the character-level encoder significantly outperforms the subword-level encoder on all the language pairs. We observe that on CS-EN, FI-EN and RU-EN, the quality of the multilingual character-level translation even surpasses the models specifically trained on that language pair alone, both in terms of the BLEU score and human judgment.
Learning Distributed Representations of Texts and Entities from Knowledge Base
Authors: Yamada Ikuya , Shindo Hiroyuki , Takeda Hideaki , Takefuji Yoshiyasu
Published: December 2018

We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.
In-Order Transition-based Constituent Parsing
Authors: Liu Jiangming , Zhang Yue
Published: December 2018

Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation tree, where bottom-up strategies and top-down strategies take post-order and pre-order traversal over trees, respectively. Bottom-up parsers benefit from rich features from readily built partial parses, but lack lookahead guidance in the parsing process; top-down parsers benefit from non-local guidance for local decisions, but rely on a strong encoder over the input to predict a constituent hierarchy before its construction. To mitigate both issues, we propose a novel parsing system based on in-order traversal over syntactic trees, designing a set of transition actions to find a compromise between bottom-up constituent information and top-down lookahead information. Based on stack-LSTM, our psycholinguistically motivated constituent parsing system achieves 91.8 F1 on the WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised reranking and 94.2 F1 with semi-supervised reranking, which are the best results on the WSJ benchmark.
Parsing with Traces: An O(n4) Algorithm and a Structural Representation
Authors: Kummerfeld Jonathan K. , Klein Dan
Published: December 2018

General treebank analyses are graph structured, but parsers are typically restricted to tree structures for efficiency and modeling reasons. We propose a new representation and algorithm for a class of graph structures that is flexible enough to cover almost all treebank structures, while still admitting efficient learning and inference. In particular, we consider directed, acyclic, one-endpoint-crossing graph structures, which cover most long-distance dislocation, shared argumentation, and similar tree-violating linguistic phenomena. We describe how to convert phrase structure parses, including traces, to our new representation in a reversible manner. Our dynamic program uniquely decomposes structures, is sound and complete, and covers 97.3% of the Penn English Treebank. We also implement a proof-of-concept parser that recovers a range of null elements and trace types.
Learning Tier-based Strictly 2-Local Languages
Authors: Jardine Adam , Heinz Jeffrey
Published: December 2018

The Tier-based Strictly 2-Local (TSL2) languages are a class of formal languages which have been shown to model long-distance phonotactic generalizations in natural language (Heinz et al., 2011). This paper introduces the Tier-based Strictly 2-Local Inference Algorithm (2TSLIA), the first nonenumerative learner for the TSL2 languages. We prove the 2TSLIA is guaranteed to converge in polynomial time on a data sample whose size is bounded by a constant.
Transforming Dependency Structures to Logical Forms for Semantic Parsing
Authors: Reddy Siva , Täckström Oscar , Collins Michael , Kwiatkowski Tom , Das Dipanjan , Steedman Mark , Lapata Mirella
Published: December 2018

The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast—partly due to the lack of a strong type system—dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages. However, the lack of a type system makes a formal mechanism for deriving logical forms from dependency structures challenging. We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees. These logical forms are then used for semantic parsing of natural language to Freebase. Experiments on the Free917 and Web-Questions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task. Compared to prior work, we obtain the strongest result to date on Free917 and competitive results on WebQuestions.
Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error States
Authors: Vaswani Ashish , Sagae Kenji
Published: December 2018

Transition-based approaches based on local classification are attractive for dependency parsing due to their simplicity and speed, despite producing results slightly below the state-of-the-art. In this paper, we propose a new approach for approximate structured inference for transition-based parsing that produces scores suitable for global scoring using local models. This is accomplished with the introduction of error states in local training, which add information about incorrect derivation paths typically left out completely in locally-trained models. Using neural networks for our local classifiers, our approach achieves 93.61% accuracy for transition-based dependency parsing in English.
J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features
Authors: Nguyen Dat Ba , Theobald Martin , Weikum Gerhard
Published: December 2018

Methods for Named Entity Recognition and Disambiguation (NERD) perform NER and NED in two separate stages. Therefore, NED may be penalized with respect to precision by NER false positives, and suffers in recall from NER false negatives. Conversely, NED does not fully exploit information computed by NER such as types of mentions. This paper presents J-NERD, a new approach to perform NER and NED jointly, by means of a probabilistic graphical model that captures mention spans, mention types, and the mapping of mentions to entities in a knowledge base. We present experiments with different kinds of texts from the CoNLL’03, ACE’05, and ClueWeb’09-FACC1 corpora. J-NERD consistently outperforms state-of-the-art competitors in end-to-end NERD precision, recall, and F1.
Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations
Authors: Marcheggiani Diego , Titov Ivan
Published: December 2018

We present a method for unsupervised open-domain relation discovery. In contrast to previous (mostly generative and agglomerative clustering) approaches, our model relies on rich contextual features and makes minimal independence assumptions. The model is composed of two parts: a feature-rich relation extractor, which predicts a semantic relation between two entities, and a factorization model, which reconstructs arguments (i.e., the entities) relying on the predicted relation. The two components are estimated jointly so as to minimize errors in recovering arguments. We study factorization models inspired by previous work in relation factorization and selectional preference modeling. Our models substantially outperform the generative and agglomerative-clustering counterparts and achieve state-of-the-art performance.
Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
Authors: Kiperwasser Eliyahu , Goldberg Yoav
Published: December 2018

We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors. The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing. We demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser as well as to a globally optimized graph-based parser. The resulting parsers have very simple architectures, and match or surpass the state-of-the-art accuracies on English and Chinese.
Named Entity Recognition with Bidirectional LSTM-CNNs
Authors: Chiu Jason P.C. , Nichols Eric
Published: December 2018

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering. We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches. Extensive evaluation shows that, given only tokenized text and publicly available word embeddings, our system is competitive on the CoNLL-2003 dataset and surpasses the previously reported state of the art performance on the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed from publicly-available sources, we establish new state of the art performance with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing systems that employ heavy feature engineering, proprietary lexicons, and rich entity linking information.
Encoding Prior Knowledge with Eigenword Embeddings
Authors: Osborne Dominique , Narayan Shashi , Cohen Shay B.
Published: December 2018

Canonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views. It has been previously used to derive word embeddings, where one view indicates a word, and the other view indicates its context. We describe a way to incorporate prior knowledge into CCA, give a theoretical justification for it, and test it by deriving word embeddings and evaluating them on a myriad of datasets.
Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health
Authors: Althoff Tim , Clark Kevin , Leskovec Jure
Published: December 2018

Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message-based counseling conversations. We develop a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes. Applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses, we discover actionable conversation strategies that are associated with better conversation outcomes.
The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages
Authors: Wang Dingquan , Eisner Jason
Published: December 2018

We release Galactic Dependencies 1.0—a large set of synthetic languages not found on Earth, but annotated in Universal Dependencies format. This new resource aims to provide training and development data for NLP methods that aim to adapt to unfamiliar languages. Each synthetic treebank is produced from a real treebank by stochastically permuting the dependents of nouns and/or verbs to match the word order of other real languages. We discuss the usefulness, realism, parsability, perplexity, and diversity of the synthetic languages. As a simple demonstration of the use of Galactic Dependencies, we consider single-source transfer, which attempts to parse a real target language using a parser trained on a “nearby” source language. We find that including synthetic source languages somewhat increases the diversity of the source pool, which significantly improves results for most target languages.
Utilizing Temporal Information for Taxonomy Construction
Authors: Tuan Luu Anh , Hui Siu Cheung , Ng See Kiong
Published: December 2018

Taxonomies play an important role in many applications by organizing domain knowledge into a hierarchy of ‘ is-a’ relations between terms. Previous work on automatic construction of taxonomies from text documents either ignored temporal information or used fixed time periods to discretize the time series of documents. In this paper, we propose a time-aware method to automatically construct and effectively maintain a taxonomy from a given series of documents preclustered for a domain of interest. The method extracts temporal information from the documents and uses a timestamp contribution function to score the temporal relevance of the evidence from source texts when identifying the taxonomic relations for constructing the taxonomy. Experimental results show that our proposed method outperforms the state-of-the-art methods by increasing F-measure up to 7%–20%. Furthermore, the proposed method can incrementally update the taxonomy by adding fresh relations from new data and removing outdated relations using an information decay function. It thus avoids rebuilding the whole taxonomy from scratch for every update and keeps the taxonomy effectively up-to-date in order to track the latest information trends in the rapidly evolving domain.
Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment
Authors: Dutta Sourav , Weikum Gerhard
Published: December 2018

Identifying and linking named entities across information sources is the basis of knowledge acquisition and at the heart of Web search, recommendations, and analytics. An important problem in this context is cross-document co-reference resolution (CCR): computing equivalence classes of textual mentions denoting the same entity, within and across documents. Prior methods employ ranking, clustering, or probabilistic graphical models using syntactic features and distant features from knowledge bases. However, these methods exhibit limitations regarding run-time and robustness.

This paper presents the CROCS framework for unsupervised CCR, improving the state of the art in two ways. First, we extend the way knowledge bases are harnessed, by constructing a notion of semantic summaries for intra-document co-reference chains using co-occurring entity mentions belonging to different chains. Second, we reduce the computational cost by a new algorithm that embeds sample-based bisection, using spectral clustering or graph partitioning, in a hierarchical clustering process. This allows scaling up CCR to large corpora. Experiments with three datasets show significant gains in output quality, compared to the best prior methods, and the run-time efficiency of CROCS.
A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment
Authors: Wang Jing , Bansal Mohit , Gimpel Kevin , Ziebart Brian D. , Yu Clement T.
Published: December 2018

Word sense induction (WSI) seeks to automatically discover the senses of a word in a corpus via unsupervised methods. We propose a sense-topic model for WSI, which treats sense and topic as two separate latent variables to be inferred jointly. Topics are informed by the entire document, while senses are informed by the local context surrounding the ambiguous word. We also discuss unsupervised ways of enriching the original corpus in order to improve model performance, including using neural word embeddings and external corpora to expand the context of each data instance. We demonstrate significant improvements over the previous state-of-the-art, achieving the best results reported to date on the SemEval-2013 WSI task.
Which Step Do I Take First? Troubleshooting with Bayesian Models
Authors: Louis Annie , Lapata Mirella
Published: December 2018

Online discussion forums and community question-answering websites provide one of the primary avenues for online users to share information. In this paper, we propose text mining techniques which aid users navigate troubleshooting-oriented data such as questions asked on forums and their suggested solutions. We introduce Bayesian generative models of the troubleshooting data and apply them to two interrelated tasks: (a) predicting the complexity of the solutions (e.g., plugging a keyboard in the computer is easier compared to installing a special driver) and (b) presenting them in a ranked order from least to most complex. Experimental results show that our models are on par with human performance on these tasks, while outperforming baselines based on solution length or readability.
A Bayesian Model of Grounded Color Semantics
Authors: McMahan Brian , Stone Matthew
Published: December 2018

Natural language meanings allow speakers to encode important real-world distinctions, but corpora of grounded language use also reveal that speakers categorize the world in different ways and describe situations with different terminology. To learn meanings from data, we therefore need to link underlying representations of meaning to models of speaker judgment and speaker choice. This paper describes a new approach to this problem: we model variability through uncertainty in categorization boundaries and distributions over preferred vocabulary. We apply the approach to a large data set of color descriptions, where statistical evaluation documents its accuracy. The results are available as a Lexicon of Uncertain Color Standards (LUX), which supports future efforts in grounded language understanding and generation by probabilistically mapping 829 English color descriptions to potentially context-sensitive regions in HSV color space.
An Unsupervised Method for Uncovering Morphological Chains
Authors: Narasimhan Karthik , Barzilay Regina , Jaakkola Tommi
Published: December 2018

Most state-of-the-art systems today produce morphological analysis based only on orthographic patterns. In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words. We model word formation in terms of morphological chains, from base words to the observed words, breaking the chains into parent-child relations. We use log-linear models with morpheme and word-level features to predict possible parents, including their modifications, for each word. The limited set of candidate parents for each word render contrastive estimation feasible. Our model consistently matches or outperforms five state-of-the-art systems on Arabic, English and Turkish.
Higher-order Lexical Semantic Models for Non-factoid Answer Reranking
Authors: Fried Daniel , Jansen Peter , Hahn-Powell Gustave , Surdeanu Mihai , Clark Peter
Published: December 2018

Lexical semantic models provide robust performance for question answering, but, in general, can only capitalize on direct evidence seen during training. For example, monolingual alignment models acquire term alignment probabilities from semi-structured data such as question-answer pairs; neural network language models learn term embeddings from unstructured text. All this knowledge is then used to estimate the semantic similarity between question and answer candidates. We introduce a higher-order formalism that allows all these lexical semantic models to chain direct evidence to construct indirect associations between question and answer texts, by casting the task as the traversal of graphs that encode direct term associations. Using a corpus of 10,000 questions from Yahoo! Answers, we experimentally demonstrate that higher-order methods are broadly applicable to alignment and language models, across both word and syntactic representations. We show that an important criterion for success is controlling for the semantic drift that accumulates during graph traversal. All in all, the proposed higher-order approach improves five out of the six lexical semantic models investigated, with relative gains of up to +13% over their first-order variants.
Problems in Current Text Simplification Research: New Data Can Help
Authors: Xu Wei , Callison-Burch Chris , Napoles Courtney
Published: December 2018

Simple Wikipedia has dominated simplification research in the past 5 years. In this opinion paper, we argue that focusing on Wikipedia limits simplification research. We back up our arguments with corpus analysis and by highlighting statements that other researchers have made in the simplification literature. We introduce a new simplification dataset that is a significant improvement over Simple Wikipedia, and present a novel quantitative-comparative approach to study the quality of simplification data resources.
Improving Topic Models with Latent Feature Word Representations
Authors: Nguyen Dat Quoc , Billingsley Richard , Du Lan , Johnson Mark
Published: December 2018

Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.
From Paraphrase Database to Compositional Paraphrase Model and Back
Authors: Wieting John , Bansal Mohit , Gimpel Kevin , Livescu Karen
Published: December 2018

The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates. However, it is still unclear how it can best be used, due to the heuristic nature of the confidences and its necessarily incomplete coverage. We propose models to leverage the phrase pairs from the PPDB to build parametric paraphrase models that score paraphrase pairs more accurately than the PPDB’s internal scores while simultaneously improving its coverage. They allow for learning phrase embeddings as well as improved word embeddings. Moreover, we introduce two new, manually annotated datasets to evaluate short-phrase paraphrasing models. Using our paraphrase model trained using PPDB, we achieve state-of-the-art results on standard word and bigram similarity tasks and beat strong baselines on our new short phrase paraphrase tasks.
Unsupervised Identification of Translationese
Authors: Rabinovich Ella , Wintner Shuly
Published: December 2018

Translated texts are distinctively different from original ones, to the extent that supervised text classification methods can distinguish between them with high accuracy. These differences were proven useful for statistical machine translation. However, it has been suggested that the accuracy of translation detection deteriorates when the classifier is evaluated outside the domain it was trained on. We show that this is indeed the case, in a variety of evaluation scenarios. We then show that unsupervised classification is highly accurate on this task. We suggest a method for determining the correct labels of the clustering outcomes, and then use the labels for voting, improving the accuracy even further. Moreover, we suggest a simple method for clustering in the challenging case of mixed-domain datasets, in spite of the dominance of domain-related features over translation-related ones. The result is an effective, fully-unsupervised method for distinguishing between original and translated texts that can be applied to new domains with reasonable accuracy.
Large-Scale Information Extraction from Textual Definitions through Deep Syntactic and Semantic Analysis
Authors: Bovi Claudio Delli , Telesca Luca , Navigli Roberto
Published: December 2018

We present DefIE, an approach to large-scale Information Extraction (IE) based on a syntactic-semantic analysis of textual definitions. Given a large corpus of definitions we leverage syntactic dependencies to reduce data sparsity, then disambiguate the arguments and content words of the relation strings, and finally exploit the resulting information to organize the acquired relations hierarchically. The output of DefIE is a high-quality knowledge base consisting of several million automatically acquired semantic relations.
Parsing to Noncrossing Dependency Graphs
Authors: Kuhlmann Marco , Jonsson Peter
Published: December 2018

We study the generalization of maximum spanning tree dependency parsing to maximum acyclic subgraphs. Because the underlying optimization problem is intractable even under an arc-factored model, we consider the restriction to noncrossing dependency graphs. Our main contribution is a cubic-time exact inference algorithm for this class. We extend this algorithm into a practical parser and evaluate its performance on four linguistic data sets used in semantic dependency parsing. We also explore a generalization of our parsing framework to dependency graphs with pagenumber at most k and show that the resulting optimization problem is NP-hard for k ≥ 2.
Parsing Algebraic Word Problems into Equations
Authors: Koncel-Kedziorski Rik , Hajishirzi Hannaneh , Sabharwal Ashish , Etzioni Oren , Ang Siena Dumas
Published: December 2018

This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global discriminative models. These models are trained on a small set of word problems and their answers, without any manual annotation, in order to choose the equation that best matches the problem text. We refer to the overall system as Alges.

We compare Alges with previous work and show that it covers the full gamut of arithmetic operations whereas Hosseini et al. (2014) only handle addition and subtraction. In addition, Alges overcomes the brittleness of the Kushman et al. (2014) approach on single-equation problems, yielding a 15% to 50% reduction in error.