pwc-archive/papers-with-abstracts huggingface dataset:
A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity
Authors: Mengnan Zhao, Aaron J. Masino, Christopher C. Yang
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Named Entity Recognition (NER), Outlier Detection, Question Answering, Relation Extraction, Word Embeddings

We investigate the quality of task specific word embeddings created with relatively small, targeted corpora. We present a comprehensive evaluation framework including both intrinsic and extrinsic evaluation that can be expanded to named entities beyond drug name. Intrinsic evaluation results tell that drug name embeddings created with a domain specific document corpus outperformed the previously published versions that derived from a very large general text corpus. Extrinsic evaluation uses word embedding for the task of drug name recognition with Bi-LSTM model and the results demonstrate the advantage of using domain-specific word embeddings as the only input feature for drug name recognition with F1-score achieving 0.91. This work suggests that it may be advantageous to derive domain specific embeddings for certain tasks even when the domain specific corpus is of limited size.
MeSH-based dataset for measuring the relevance of text retrieval
Authors: Won Gyu Kim, Lana Yeganova, Donald Comeau, W. John Wilbur, Zhiyong Lu
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Information Retrieval, Retrieval, Text Retrieval

Creating simulated search environments has been of a significant interest in infor-mation retrieval, in both general and bio-medical search domains. Existing collec-tions include modest number of queries and are constructed by manually evaluat-ing retrieval results. In this work we pro-pose leveraging MeSH term assignments for creating synthetic test beds. We select a suitable subset of MeSH terms as queries, and utilize MeSH term assignments as pseudo-relevance rankings for retrieval evaluation. Using well studied retrieval functions, we show that their performance on the proposed data is consistent with similar findings in previous work. We further use the proposed retrieval evaluation framework to better understand how to combine heterogeneous sources of textual information.
CRF-LSTM Text Mining Method Unveiling the Pharmacological Mechanism of Off-target Side Effect of Anti-Multiple Myeloma Drugs
Authors: Kaiyin Zhou, Sheng Zhang, Xiangyu Meng, Qi Luo, Yuxing Wang, Ke Ding, Yukun Feng, Mo Chen, Kevin Cohen, Jingbo Xia
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: NER

Sequence labeling of biomedical entities, e.g., side effects or phenotypes, was a long-term task in BioNLP and MedNLP communities. Thanks to effects made among these communities, adverse reaction NER has developed dramatically in recent years. As an illuminative application, to achieve knowledge discovery via the combination of the text mining result and bioinformatics idea shed lights on the pharmacological mechanism research.
Prediction Models for Risk of Type-2 Diabetes Using Health Claims
Authors: Masatoshi Nagata, Kohichi Takai, Keiji Yasuda, Panikos Heracleous, Akio Yoneyama
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Prediction, Vocal Bursts Type Prediction

This study focuses on highly accurate prediction of the onset of type-2 diabetes. We investigated whether prediction accuracy can be improved by utilizing lab test data obtained from health checkups and incorporating health claim text data such as medically diagnosed diseases with ICD10 codes and pharmacy information. In a previous study, prediction accuracy was increased slightly by adding diagnosis disease name and independent variables such as prescription medicine. Therefore, in the current study we explored more suitable models for prediction by using state-of-the-art techniques such as XGBoost and long short-term memory (LSTM) based on recurrent neural networks. In the current study, text data was vectorized using word2vec, and the prediction model was compared with logistic regression. The results obtained confirmed that onset of type-2 diabetes can be predicted with a high degree of accuracy when the XGBoost model is used.
On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data
Authors: Yaqiang Wang, Yunhui Chen, Hongping Shu, Yongguang Jiang
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Disease Prediction, Information Retrieval, Language Modeling, Language Modelling, Word Embeddings

High quality word embeddings are of great significance to advance applications of biomedical natural language processing. In recent years, a surge of interest on how to learn good embeddings and evaluate embedding quality based on English medical text has become increasing evident, however a limited number of studies based on Chinese medical text, particularly Chinese clinical records, were performed. Herein, we proposed a novel approach of improving the quality of learned embeddings using out-domain data as a supplementary in the case of limited Chinese clinical records. Moreover, the embedding quality evaluation method was conducted based on Medical Conceptual Similarity Property. The experimental results revealed that selecting good training samples was necessary, and collecting right amount of out-domain data and trading off between the quality of embeddings and the training time consumption were essential factors for better embeddings.
Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts
Authors: Hai-Long Trieu, Nhung T. H. Nguyen, Makoto Miwa, Sophia Ananiadou
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: coreference-resolution, Coreference Resolution, Word Embeddings

Existing biomedical coreference resolution systems depend on features and/or rules based on syntactic parsers. In this paper, we investigate the utility of the state-of-the-art general domain neural coreference resolution system on biomedical texts. The system is an end-to-end system without depending on any syntactic parsers. We also investigate the domain specific features to enhance the system for biomedical texts. Experimental results on the BioNLP Protein Coreference dataset and the CRAFT corpus show that, with no parser information, the adapted system compared favorably with the systems that depend on parser information on these datasets, achieving 51.23{\%} on the BioNLP dataset and 36.33{\%} on the CRAFT corpus in F1 score. In-domain embeddings and domain-specific features helped improve the performance on the BioNLP dataset, but they did not on the CRAFT corpus.
Toward Cross-Domain Engagement Analysis in Medical Notes
Authors: Sara Rosenthal, Adam Faulkner
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Domain Adaptation, General Classification

We present a novel annotation task evaluating a patient{'}s engagement with their health care regimen. The concept of engagement supplements the traditional concept of adherence with a focus on the patient{'}s affect, lifestyle choices, and health goal status. We describe an engagement annotation task across two patient note domains: traditional clinical notes and a novel domain, care manager notes, where we find engagement to be more common. The annotation task resulted in a kappa of .53, suggesting strong annotator intuitions regarding engagement-bearing language. In addition, we report the results of a series of preliminary engagement classification experiments using domain adaptation.
Automatic Extraction of Entities and Relation from Legal Documents
Authors: Judith Jeyafreeda Andrew
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Named Entity Recognition (NER), Relation

In recent years, the journalists and computer sciences speak to each other to identify useful technologies which would help them in extracting useful information. This is called {``}computational Journalism{''}. In this paper, we present a method that will enable the journalists to automatically identifies and annotates entities such as names of people, organizations, role and functions of people in legal documents; the relationship between these entities are also explored. The system uses a combination of both statistical and rule based technique. The statistical method used is Conditional Random Fields and for the rule based technique, document and language specific regular expressions are used.
A Sequence Learning Method for Domain-Specific Entity Linking
Authors: Emrah Inan, Oguz Dikenelli
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Entity Disambiguation, Entity Linking, Knowledge Graph Completion, Link Prediction, Relation Extraction, Word Embeddings

Recent collective Entity Linking studies usually promote global coherence of all the mapped entities in the same document by using semantic embeddings and graph-based approaches. Although graph-based approaches are shown to achieve remarkable results, they are computationally expensive for general datasets. Also, semantic embeddings only indicate relatedness between entity pairs without considering sequences. In this paper, we address these problems by introducing a two-fold neural model. First, we match easy mention-entity pairs and using the domain information of this pair to filter candidate entities of closer mentions. Second, we resolve more ambiguous pairs using bidirectional Long Short-Term Memory and CRF models for the entity disambiguation. Our proposed system outperforms state-of-the-art systems on the generated domain-specific evaluation dataset.
Attention-based Semantic Priming for Slot-filling
Authors: Jiewen Wu, Rafael E. Banchs, Luis Fern D{'}Haro, o, Pavitra Krishnaswamy, Nancy Chen
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: slot-filling, Slot Filling, Spoken Language Understanding, Word Embeddings

The problem of sequence labelling in language understanding would benefit from approaches inspired by semantic priming phenomena. We propose that an attention-based RNN architecture can be used to simulate semantic priming for sequence labelling. Specifically, we employ pre-trained word embeddings to characterize the semantic relationship between utterances and labels. We validate the approach using varying sizes of the ATIS and MEDIA datasets, and show up to 1.4-1.9{\%} improvement in F1 score. The developed framework can enable more explainable and generalizable spoken language understanding systems.
Named Entity Recognition for Hindi-English Code-Mixed Social Media Text
Authors: Vinay Singh, Deepanshu Vijay, Syed Sarfaraz Akhtar, Manish Shrivastava
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: BIG-bench Machine Learning, Entity Extraction using GAN, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named Entity Recognition (NER) is a major task in the field of Natural Language Processing (NLP), and also is a sub-task of Information Extraction. The challenge of NER for tweets lie in the insufficient information available in a tweet. There has been a significant amount of work done related to entity extraction, but only for resource rich languages and domains such as newswire. Entity extraction is, in general, a challenging task for such an informal text, and code-mixed text further complicates the process with it{'}s unstructured and incomplete information. We propose experiments with different machine learning classification algorithms with word, character and lexical features. The algorithms we experimented with are Decision tree, Long Short-Term Memory (LSTM), and Conditional Random Field (CRF). In this paper, we present a corpus for NER in Hindi-English Code-Mixed along with extensive experiments on our machine learning models which achieved the best f1-score of 0.95 with both CRF and LSTM.
Forms of Anaphoric Reference to Organisational Named Entities: Hoping to widen appeal, they diversified
Authors: Christian Hardmeier, Luca Bevacqua, Sharid Lo{\'a}iciga, Hannah Rohde
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Story Continuation

Proper names of organisations are a special case of collective nouns. Their meaning can be conceptualised as a collective unit or as a plurality of persons, allowing for different morphological marking of coreferent anaphoric pronouns. This paper explores the variability of references to organisation names with 1) a corpus analysis and 2) two crowd-sourced story continuation experiments. The first shows that the preference for singular vs. plural conceptualisation is dependent on the level of formality of a text. In the second, we observe a strong preference for the plural they otherwise typical of informal speech. Using edited corpus data instead of constructed sentences as stimuli reduces this preference.
Named-Entity Tagging and Domain adaptation for Better Customized Translation
Authors: Zhongwei Li, Xuancong Wang, Ai Ti Aw, Eng Siong Chng, Haizhou Li
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Domain Adaptation, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, NMT, Sentence Embedding, Translation

Customized translation need pay spe-cial attention to the target domain ter-minology especially the named-entities for the domain. Adding linguistic features to neural machine translation (NMT) has been shown to benefit translation in many studies. In this paper, we further demonstrate that adding named-entity (NE) feature with named-entity recognition (NER) into the source language produces better translation with NMT. Our experiments show that by just including the different NE classes and boundary tags, we can increase the BLEU score by around 1 to 2 points using the standard test sets from WMT2017. We also show that adding NE tags using NER and applying in-domain adaptation can be combined to further improve customized machine translation.
NEWS 2018 Whitepaper
Authors: Nancy Chen, Xiangyu Duan, Min Zhang, Rafael E. Banchs, Haizhou Li
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Benchmarking, Machine Translation, Translation, Transliteration

Transliteration is defined as phonetic translation of names across languages. Transliteration of Named Entities (NEs) is necessary in many applications, such as machine translation, corpus alignment, cross-language IR, information extraction and automatic lexicon acquisition. All such systems call for high-performance transliteration, which is the focus of shared task in the NEWS 2018 workshop. The objective of the shared task is to promote machine transliteration research by providing a common benchmarking platform for the community to evaluate the state-of-the-art technologies.
Report of NEWS 2018 Named Entity Transliteration Shared Task
Authors: Nancy Chen, Rafael E. Banchs, Min Zhang, Xiangyu Duan, Haizhou Li
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Information Retrieval, Machine Translation, Transliteration

This report presents the results from the Named Entity Transliteration Shared Task conducted as part of The Seventh Named Entities Workshop (NEWS 2018) held at ACL 2018 in Melbourne, Australia. Similar to previous editions of NEWS, the Shared Task featured 19 tasks on proper name transliteration, including 13 different languages and two different Japanese scripts. A total of 6 teams from 8 different institutions participated in the evaluation, submitting 424 runs, involving different transliteration methodologies. Four performance metrics were used to report the evaluation results. The NEWS shared task on machine transliteration has successfully achieved its objectives by providing a common ground for the research community to conduct comparative evaluations of state-of-the-art technologies that will benefit the future research and development in this area.
Statistical Machine Transliteration Baselines for NEWS 2018
Authors: Snigdha Singhania, Minh Nguyen, Gia H. Ngo, Nancy Chen
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Information Retrieval, Machine Translation, Re-Ranking, Transliteration

This paper reports the results of our trans-literation experiments conducted on NEWS 2018 Shared Task dataset. We focus on creating the baseline systems trained using two open-source, statistical transliteration tools, namely Sequitur and Moses. We discuss the pre-processing steps performed on this dataset for both the systems. We also provide a re-ranking system which uses top hypotheses from Sequitur and Moses to create a consolidated list of transliterations. The results obtained from each of these models can be used to present a good starting point for the participating teams.
A Deep Learning Based Approach to Transliteration
Authors: Soumyadeep Kundu, Sayantan Paul, Santanu Pal
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Deep Learning, Information Retrieval, Machine Translation, NMT, Translation, Transliteration

In this paper, we propose different architectures for language independent machine transliteration which is extremely important for natural language processing (NLP) applications. Though a number of statistical models for transliteration have already been proposed in the past few decades, we proposed some neural network based deep learning architectures for the transliteration of named entities. Our transliteration systems adapt two different neural machine translation (NMT) frameworks: recurrent neural network and convolutional sequence to sequence based NMT. It is shown that our method provides quite satisfactory results when it comes to multi lingual machine transliteration. Our submitted runs are an ensemble of different transliteration systems for all the language pairs. In the NEWS 2018 Shared Task on Transliteration, our method achieves top performance for the En{--}Pe and Pe{--}En language pairs and comparable results for other cases.
Comparison of Assorted Models for Transliteration
Authors: Saeed Najafi, Bradley Hauer, Rashed Rubby Riyadh, Leyuan Yu, Grzegorz Kondrak
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Transliteration

We report the results of our experiments in the context of the NEWS 2018 Shared Task on Transliteration. We focus on the comparison of several diverse systems, including three neural MT models. A combination of discriminative, generative, and neural models obtains the best results on the development sets. We also put forward ideas for improving the shared task.
Neural Machine Translation Techniques for Named Entity Transliteration
Authors: Roman Grundkiewicz, Kenneth Heafield
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Post-Editing, Decoder, Grammatical Error Correction, Machine Translation, NMT, Translation, Transliteration

Transliterating named entities from one language into another can be approached as neural machine translation (NMT) problem, for which we use deep attentional RNN encoder-decoder models. To build a strong transliteration system, we apply well-established techniques from NMT, such as dropout regularization, model ensembling, rescoring with right-to-left models, and back-translation. Our submission to the NEWS 2018 Shared Task on Named Entity Transliteration ranked first in several tracks.
Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages
Authors: Ngoc Tan Le, Fatiha Sadat
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, text-to-speech, Text to Speech, Translation, Transliteration, Word Embeddings

Grapheme-to-phoneme models are key components in automatic speech recognition and text-to-speech systems. With low-resource language pairs that do not have available and well-developed pronunciation lexicons, grapheme-to-phoneme models are particularly useful. These models are based on initial alignments between grapheme source and phoneme target sequences. Inspired by sequence-to-sequence recurrent neural network-based translation methods, the current research presents an approach that applies an alignment representation for input sequences and pre-trained source and target embeddings to overcome the transliteration problem for a low-resource languages pair. We participated in the NEWS 2018 shared task for the English-Vietnamese transliteration task.
Stop Word Lists in Free Open-source Software Packages
Authors: Joel Nothman, Hanmin Qin, Roman Yurchak
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

Open-source software packages for language processing often include stop word lists. Users may apply them without awareness of their surprising omissions (e.g. {``}hasn{'}t{''} but not {``}hadn{'}t{''}) and inclusions ({``}computer{''}), or their incompatibility with a particular tokenizer. Motivated by issues raised about the Scikit-learn stop list, we investigate variation among and consistency within 52 popular English-language stop lists, and propose strategies for mitigating these issues.
Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation
Authors: Zhiting Hu, Zichao Yang, Tiancheng Zhao, Haoran Shi, Junxian He, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Lianhui Qin, Devendra Singh Chaplot, Bowen Tan, Xingjiang Yu, Eric Xing
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Image Captioning, Machine Translation, Text Generation, Text Summarization, Translation

We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks. Different from many existing toolkits that are specialized for specific applications (e.g., neural machine translation), Texar is designed to be highly flexible and versatile. This is achieved by abstracting the common patterns underlying the diverse tasks and methodologies, creating a library of highly reusable modules and functionalities, and enabling arbitrary model architectures and various algorithmic paradigms. The features make Texar particularly suitable for technique sharing and generalization across different text generation applications. The toolkit emphasizes heavily on extensibility and modularized system design, so that components can be freely plugged in or swapped out. We conduct extensive experiments and case studies to demonstrate the use and advantage of the toolkit.
Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP
Authors: Daniel Pressel, Sagnik Ray Choudhury, Brian Lester, Yanjie Zhao, Matt Barta
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Deep Learning, Language Modeling, Language Modelling, Machine Translation, Named Entity Recognition (NER), Part-Of-Speech Tagging, Slot Filling

We introduce Baseline: a library for reproducible deep learning research and fast model development for NLP. The library provides easily extensible abstractions and implementations for data loading, model development, training and export of deep learning architectures. It also provides implementations for simple, high-performance, deep learning models for various NLP tasks, against which newly developed models can be compared. Deep learning experiments are hard to reproduce, Baseline provides functionalities to track them. The goal is to allow a researcher to focus on model development, delegating the repetitive tasks to the library.
OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models
Authors: Oleksii Kuchaiev, Boris Ginsburg, Igor Gitman, Vitaly Lavrukhin, Carl Case, Paulius Micikevicius
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Machine Translation, speech-recognition, Speech Recognition, Translation

We present OpenSeq2Seq {--} an open-source toolkit for training sequence-to-sequence models. The main goal of our toolkit is to allow researchers to most effectively explore different sequence-to-sequence architectures. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq provides building blocks for training encoder-decoder models for neural machine translation and automatic speech recognition. We plan to extend it with other modalities in the future.