Paper Group NANR 117
Are doggies really nicer than dogs? The impact of morphological derivation on emotional valence in German. Situating Word Senses in their Historical Context with Linked Data. NLPTEA 2017 Shared Task – Chinese Spelling Check. Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. An End-to-end Environment for Rese …
Are doggies really nicer than dogs? The impact of morphological derivation on emotional valence in German
| Title | Are doggies really nicer than dogs? The impact of morphological derivation on emotional valence in German |
| Authors | Gabriella Lapesa, Sebastian Pad{'o}, Tillmann Pross, Antje Rossdeutscher |
| Abstract | |
| Tasks | Sentiment Analysis |
| Published | 2017-01-01 |
| URL | https://www.aclweb.org/anthology/W17-6922/ |
| https://www.aclweb.org/anthology/W17-6922 | |
| PWC | https://paperswithcode.com/paper/are-doggies-really-nicer-than-dogs-the-impact |
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Situating Word Senses in their Historical Context with Linked Data
| Title | Situating Word Senses in their Historical Context with Linked Data |
| Authors | Fahad Khan, Jack Bowers, Francesca Frontini |
| Abstract | |
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| Published | 2017-01-01 |
| URL | https://www.aclweb.org/anthology/W17-6916/ |
| https://www.aclweb.org/anthology/W17-6916 | |
| PWC | https://paperswithcode.com/paper/situating-word-senses-in-their-historical |
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NLPTEA 2017 Shared Task – Chinese Spelling Check
| Title | NLPTEA 2017 Shared Task – Chinese Spelling Check |
| Authors | Gabriel Fung, Maxime Debosschere, Dingmin Wang, Bo Li, Jia Zhu, Kam-Fai Wong |
| Abstract | This paper provides an overview along with our findings of the Chinese Spelling Check shared task at NLPTEA 2017. The goal of this task is to develop a computer-assisted system to automatically diagnose typing errors in traditional Chinese sentences written by students. We defined six types of errors which belong to two categories. Given a sentence, the system should detect where the errors are, and for each detected error determine its type and provide correction suggestions. We designed, constructed, and released a benchmark dataset for this task. |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-5905/ |
| https://www.aclweb.org/anthology/W17-5905 | |
| PWC | https://paperswithcode.com/paper/nlptea-2017-shared-task-a-chinese-spelling |
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Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German
| Title | Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German |
| Authors | Nina Seemann, Marie-Luis Merten, Michaela Geierhos, Doris Tophinke, Eyke H{"u}llermeier |
| Abstract | In this paper, we present the annotation challenges we have encountered when working on a historical language that was undergoing elaboration processes. We especially focus on syntactic ambiguity and gradience in Middle Low German, which causes uncertainty to some extent. Since current annotation tools consider construction contexts and the dynamics of the grammaticalization only partially, we plan to extend CorA - a web-based annotation tool for historical and other non-standard language data - to capture elaboration phenomena and annotator unsureness. Moreover, we seek to interactively learn morphological as well as syntactic annotations. |
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| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2206/ |
| https://www.aclweb.org/anthology/W17-2206 | |
| PWC | https://paperswithcode.com/paper/annotation-challenges-for-reconstructing-the |
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An End-to-end Environment for Research Question-Driven Entity Extraction and Network Analysis
| Title | An End-to-end Environment for Research Question-Driven Entity Extraction and Network Analysis |
| Authors | Andre Blessing, Nora Echelmeyer, Markus John, Nils Reiter |
| Abstract | This paper presents an approach to extract co-occurrence networks from literary texts. It is a deliberate decision not to aim for a fully automatic pipeline, as the literary research questions need to guide both the definition of the nature of the things that co-occur as well as how to decide co-occurrence. We showcase the approach on a Middle High German romance, \textit{Parzival}. Manual inspection and discussion shows the huge impact various choices have. |
| Tasks | Dependency Parsing, Entity Extraction |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2208/ |
| https://www.aclweb.org/anthology/W17-2208 | |
| PWC | https://paperswithcode.com/paper/an-end-to-end-environment-for-research |
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ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter
| Title | ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter |
| Authors | Enkhzol Dovdon, Jos{'e} Saias |
| Abstract | This paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic-Based Message Polarity Classification according to a two-point scale) of SemEval-2017 Task 4 Sentiment Analysis in Twitter. We used several features with a sentiment lexicon and NLP techniques, Maximum Entropy as a classifier for our system. |
| Tasks | Aspect-Based Sentiment Analysis, Sentiment Analysis, Text Classification |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2106/ |
| https://www.aclweb.org/anthology/S17-2106 | |
| PWC | https://paperswithcode.com/paper/ej-sa-2017-at-semeval-2017-task-4-experiments |
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SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets
| Title | SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets |
| Authors | Narges Tabari, Armin Seyeditabari, Wlodek Zadrozny |
| Abstract | Sentiment analysis is the process of identifying the opinion expressed in text. Recently it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. SemEval-2017 task 5 focuses on the financial market as the domain for sentiment analysis of text; specifically, task 5, subtask 1 focuses on financial tweets about stock symbols. In this paper, we describe a machine learning classifier for binary classification of financial tweets. We used natural language processing techniques and the random forest algorithm to train our model, and tuned it for the training dataset of Task 5, subtask 1. Our system achieves the 7th rank on the leaderboard of the task. |
| Tasks | Sentiment Analysis, Text Categorization |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2146/ |
| https://www.aclweb.org/anthology/S17-2146 | |
| PWC | https://paperswithcode.com/paper/sentiheros-at-semeval-2017-task-5-an |
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Target word prediction and paraphasia classification in spoken discourse
| Title | Target word prediction and paraphasia classification in spoken discourse |
| Authors | Joel Adams, Steven Bedrick, Gerasimos Fergadiotis, Kyle Gorman, Jan van Santen |
| Abstract | We present a system for automatically detecting and classifying phonologically anomalous productions in the speech of individuals with aphasia. Working from transcribed discourse samples, our system identifies neologisms, and uses a combination of string alignment and language models to produce a lattice of plausible words that the speaker may have intended to produce. We then score this lattice according to various features, and attempt to determine whether the anomalous production represented a phonemic error or a genuine neologism. This approach has the potential to be expanded to consider other types of paraphasic errors, and could be applied to a wide variety of screening and therapeutic applications. |
| Tasks | Semantic Textual Similarity, Spoken Language Understanding |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2301/ |
| https://www.aclweb.org/anthology/W17-2301 | |
| PWC | https://paperswithcode.com/paper/target-word-prediction-and-paraphasia |
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Project Notes on building a conversational parser on top of a text parser: Towards a causal language tagger for spoken Chinese
| Title | Project Notes on building a conversational parser on top of a text parser: Towards a causal language tagger for spoken Chinese |
| Authors | Andreas Liesenfeld |
| Abstract | |
| Tasks | Semantic Parsing |
| Published | 2017-01-01 |
| URL | https://www.aclweb.org/anthology/W17-7407/ |
| https://www.aclweb.org/anthology/W17-7407 | |
| PWC | https://paperswithcode.com/paper/project-notes-on-building-a-conversational |
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N-gram Model for Chinese Grammatical Error Diagnosis
| Title | N-gram Model for Chinese Grammatical Error Diagnosis |
| Authors | Jianbo Zhao, Hao Liu, Zuyi Bao, Xiaopeng Bai, Si Li, Zhiqing Lin |
| Abstract | Detection and correction of Chinese grammatical errors have been two of major challenges for Chinese automatic grammatical error diagnosis.This paper presents an N-gram model for automatic detection and correction of Chinese grammatical errors in NLPTEA 2017 task. The experiment results show that the proposed method is good at correction of Chinese grammatical errors. |
| Tasks | Language Modelling |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-5907/ |
| https://www.aclweb.org/anthology/W17-5907 | |
| PWC | https://paperswithcode.com/paper/n-gram-model-for-chinese-grammatical-error |
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A New Error Annotation for Dyslexic texts in Arabic
| Title | A New Error Annotation for Dyslexic texts in Arabic |
| Authors | Maha Alamri, William J Teahan |
| Abstract | This paper aims to develop a new classification of errors made in Arabic by those suffering from dyslexia to be used in the annotation of the Arabic dyslexia corpus (BDAC). The dyslexic error classification for Arabic texts (DECA) comprises a list of spelling errors extracted from previous studies and a collection of texts written by people with dyslexia that can provide a framework to help analyse specific errors committed by dyslexic writers. The classification comprises 37 types of errors, grouped into nine categories. The paper also discusses building a corpus of dyslexic Arabic texts that uses the error annotation scheme and provides an analysis of the errors that were found in the texts. |
| Tasks | |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1309/ |
| https://www.aclweb.org/anthology/W17-1309 | |
| PWC | https://paperswithcode.com/paper/a-new-error-annotation-for-dyslexic-texts-in |
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Stacking With Auxiliary Features for Entity Linking in the Medical Domain
| Title | Stacking With Auxiliary Features for Entity Linking in the Medical Domain |
| Authors | Nazneen Fatema Rajani, Mihaela Bornea, Ken Barker |
| Abstract | Linking spans of natural language text to concepts in a structured source is an important task for many problems. It allows intelligent systems to leverage rich knowledge available in those sources (such as concept properties and relations) to enhance the semantics of the mentions of these concepts in text. In the medical domain, it is common to link text spans to medical concepts in large, curated knowledge repositories such as the Unified Medical Language System. Different approaches have different strengths: some are precision-oriented, some recall-oriented; some better at considering context but more prone to hallucination. The variety of techniques suggests that ensembling could outperform component technologies at this task. In this paper, we describe our process for building a Stacking ensemble using additional, auxiliary features for Entity Linking in the medical domain. We report experiments that show that naive ensembling does not always outperform component Entity Linking systems, that stacking usually outperforms naive ensembling, and that auxiliary features added to the stacker further improve its performance on three distinct datasets. Our best model produces state-of-the-art results on several medical datasets. |
| Tasks | Entity Linking |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2305/ |
| https://www.aclweb.org/anthology/W17-2305 | |
| PWC | https://paperswithcode.com/paper/stacking-with-auxiliary-features-for-entity |
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Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus
| Title | Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus |
| Authors | Runze Zhang, Siyu Zhu, Tian Fang, Long Quan |
| Abstract | The increasing scale of Structure-from-Motion is fundamentally limited by the conventional optimization framework for the all-in-one global bundle adjustment. In this paper, we propose a distributed approach to coping with this global bundle adjustment for very large scale Structure-from-Motion computation. First, we derive the distributed formulation from the classical optimization algorithm ADMM, Alternating Direction Method of Multipliers, based on the global camera consensus. Then, we analyze the conditions under which the convergence of this distributed optimization would be guaranteed. In particular, we adopt over-relaxation and self-adaption schemes to improve the convergence rate. After that, we propose to split the large scale camera-point visibility graph in order to reduce the communication overheads of the distributed computing. The experiments on both public large scale SfM data-sets and our very large scale aerial photo sets demonstrate that the proposed distributed method clearly outperforms the state-of-the-art method in efficiency and accuracy. |
| Tasks | Distributed Optimization |
| Published | 2017-10-01 |
| URL | http://openaccess.thecvf.com/content_iccv_2017/html/Zhang_Distributed_Very_Large_ICCV_2017_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhang_Distributed_Very_Large_ICCV_2017_paper.pdf | |
| PWC | https://paperswithcode.com/paper/distributed-very-large-scale-bundle |
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When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages
| Title | When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages |
| Authors | Maria Medvedeva, Martin Kroon, Barbara Plank |
| Abstract | We present the results of our participation in the VarDial 4 shared task on discriminating closely related languages. Our submission includes simple traditional models using linear support vector machines (SVMs) and a neural network (NN). The main idea was to leverage language group information. We did so with a two-layer approach in the traditional model and a multi-task objective in the neural network case. Our results confirm earlier findings: simple traditional models outperform neural networks consistently for this task, at least given the amount of systems we could examine in the available time. Our two-layer linear SVM ranked 2nd in the shared task. |
| Tasks | Language Identification |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1219/ |
| https://www.aclweb.org/anthology/W17-1219 | |
| PWC | https://paperswithcode.com/paper/when-sparse-traditional-models-outperform |
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Welfare Guarantees from Data
| Title | Welfare Guarantees from Data |
| Authors | Darrell Hoy, Denis Nekipelov, Vasilis Syrgkanis |
| Abstract | Analysis of efficiency of outcomes in game theoretic settings has been a main item of study at the intersection of economics and computer science. The notion of the price of anarchy takes a worst-case stance to efficiency analysis, considering instance independent guarantees of efficiency. We propose a data-dependent analog of the price of anarchy that refines this worst-case assuming access to samples of strategic behavior. We focus on auction settings, where the latter is non-trivial due to the private information held by participants. Our approach to bounding the efficiency from data is robust to statistical errors and mis-specification. Unlike traditional econometrics, which seek to learn the private information of players from observed behavior and then analyze properties of the outcome, we directly quantify the inefficiency without going through the private information. We apply our approach to datasets from a sponsored search auction system and find empirical results that are a significant improvement over bounds from worst-case analysis. |
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| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/6967-welfare-guarantees-from-data |
| http://papers.nips.cc/paper/6967-welfare-guarantees-from-data.pdf | |
| PWC | https://paperswithcode.com/paper/welfare-guarantees-from-data |
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