Paper Group NANR 144
The Making of the Royal Society Corpus. Investigating Opinion Mining through Language Varieties: a Case Study of Brazilian and European Portuguese tweets. 反義詞「多」和「少」在數量名結構中的不對稱現象－－以語料庫為本的分析 (The Asymmetric Occurences of \textitDou1 and \textitShao3 in the [Numeral + Measure Word/Classifier + Noun] Construction: A Corpus-based Analysis) [In Chinese] …
The Making of the Royal Society Corpus
| Title | The Making of the Royal Society Corpus |
| Authors | J{"o}rg Knappen, Stefan Fischer, Hannah Kermes, Elke Teich, Peter Fankhauser |
| Abstract | |
| Tasks | Optical Character Recognition, Part-Of-Speech Tagging, Word Embeddings |
| Published | 2017-05-01 |
| URL | https://www.aclweb.org/anthology/W17-0503/ |
| https://www.aclweb.org/anthology/W17-0503 | |
| PWC | https://paperswithcode.com/paper/the-making-of-the-royal-society-corpus |
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Investigating Opinion Mining through Language Varieties: a Case Study of Brazilian and European Portuguese tweets
| Title | Investigating Opinion Mining through Language Varieties: a Case Study of Brazilian and European Portuguese tweets |
| Authors | Douglas Vit{'o}rio, Ellen Polliana Ramos Souza, Ingryd Pereira, Adriano Oliveira |
| Abstract | |
| Tasks | Decision Making, Opinion Mining, Sentiment Analysis |
| Published | 2017-10-01 |
| URL | https://www.aclweb.org/anthology/W17-6607/ |
| https://www.aclweb.org/anthology/W17-6607 | |
| PWC | https://paperswithcode.com/paper/investigating-opinion-mining-through-language |
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反義詞「多」和「少」在數量名結構中的不對稱現象－－以語料庫為本的分析 (The Asymmetric Occurences of \textitDou1 and \textitShao3 in the [Numeral + Measure Word/Classifier + Noun] Construction: A Corpus-based Analysis) [In Chinese]
| Title | 反義詞「多」和「少」在數量名結構中的不對稱現象－－以語料庫為本的分析 (The Asymmetric Occurences of \textitDou1 and \textitShao3 in the [Numeral + Measure Word/Classifier + Noun] Construction: A Corpus-based Analysis) [In Chinese] |
| Authors | Wei-Yu Chen, Siaw-Fong Chung |
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| Published | 2017-06-01 |
| URL | https://www.aclweb.org/anthology/O17-2002/ |
| https://www.aclweb.org/anthology/O17-2002 | |
| PWC | https://paperswithcode.com/paper/ac34eaaaaaaaa-eaca-ca-ac-c34ei14i14aeaaococa |
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Approximation Algorithms for \ell_0-Low Rank Approximation
| Title | Approximation Algorithms for \ell_0-Low Rank Approximation |
| Authors | Karl Bringmann, Pavel Kolev, David Woodruff |
| Abstract | We study the $\ell_0$-Low Rank Approximation Problem, where the goal is, given an $m \times n$ matrix $A$, to output a rank-$k$ matrix $A'$ for which $\A’-A_0$ is minimized. Here, for a matrix $B$, $\B_0$ denotes the number of its non-zero entries. This NP-hard variant of low rank approximation is natural for problems with no underlying metric, and its goal is to minimize the number of disagreeing data positions. We provide approximation algorithms which significantly improve the running time and approximation factor of previous work. For $k > 1$, we show how to find, in poly$(mn)$ time for every $k$, a rank $O(k \log(n/k))$ matrix $A'$ for which $\A’-A_0 \leq O(k^2 \log(n/k)) \OPT$. To the best of our knowledge, this is the first algorithm with provable guarantees for the $\ell_0$-Low Rank Approximation Problem for $k > 1$, even for bicriteria algorithms. For the well-studied case when $k = 1$, we give a $(2+\epsilon)$-approximation in {\it sublinear time}, which is impossible for other variants of low rank approximation such as for the Frobenius norm. We strengthen this for the well-studied case of binary matrices to obtain a $(1+O(\psi))$-approximation in sublinear time, where $\psi = \OPT/\nnz{A}$. For small $\psi$, our approximation factor is $1+o(1)$. |
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| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/7242-approximation-algorithms-for-ell_0-low-rank-approximation |
| http://papers.nips.cc/paper/7242-approximation-algorithms-for-ell_0-low-rank-approximation.pdf | |
| PWC | https://paperswithcode.com/paper/approximation-algorithms-for-ell_0-low-rank-1 |
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Learning What’s Easy: Fully Differentiable Neural Easy-First Taggers
| Title | Learning What’s Easy: Fully Differentiable Neural Easy-First Taggers |
| Authors | Andr{'e} F. T. Martins, Julia Kreutzer |
| Abstract | We introduce a novel neural easy-first decoder that learns to solve sequence tagging tasks in a flexible order. In contrast to previous easy-first decoders, our models are end-to-end differentiable. The decoder iteratively updates a {``}sketch{''} of the predictions over the sequence. At its core is an attention mechanism that controls which parts of the input are strategically the best to process next. We present a new constrained softmax transformation that ensures the same cumulative attention to every word, and show how to efficiently evaluate and backpropagate over it. Our models compare favourably to BILSTM taggers on three sequence tagging tasks. | |
| Tasks | Imitation Learning, Named Entity Recognition, Part-Of-Speech Tagging |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/D17-1036/ |
| https://www.aclweb.org/anthology/D17-1036 | |
| PWC | https://paperswithcode.com/paper/learning-whats-easy-fully-differentiable |
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A Computational Model of Human Preferences for Pronoun Resolution
| Title | A Computational Model of Human Preferences for Pronoun Resolution |
| Authors | Olga Seminck, Pascal Amsili |
| Abstract | We present a cognitive computational model of pronoun resolution that reproduces the human interpretation preferences of the Subject Assignment Strategy and the Parallel Function Strategy. Our model relies on a probabilistic pronoun resolution system trained on corpus data. Factors influencing pronoun resolution are represented as features weighted by their relative importance. The importance the model gives to the preferences is in line with psycholinguistic studies. We demonstrate the cognitive plausibility of the model by running it on experimental items and simulating antecedent choice and reading times of human participants. Our model can be used as a new means to study pronoun resolution, because it captures the interaction of preferences. |
| Tasks | Coreference Resolution |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/E17-4006/ |
| https://www.aclweb.org/anthology/E17-4006 | |
| PWC | https://paperswithcode.com/paper/a-computational-model-of-human-preferences |
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Aligning Entity Names with Online Aliases on Twitter
| Title | Aligning Entity Names with Online Aliases on Twitter |
| Authors | Kevin McKelvey, Peter Goutzounis, Stephen da Cruz, Nathanael Chambers |
| Abstract | This paper presents new models that automatically align online aliases with their real entity names. Many research applications rely on identifying entity names in text, but people often refer to entities with unexpected nicknames and aliases. For example, The King and King James are aliases for Lebron James, a professional basketball player. Recent work on entity linking attempts to resolve mentions to knowledge base entries, like a wikipedia page, but linking is unfortunately limited to well-known entities with pre-built pages. This paper asks a more basic question: can aliases be aligned without background knowledge of the entity? Further, can the semantics surrounding alias mentions be used to inform alignments? We describe statistical models that make decisions based on the lexicographic properties of the aliases with their semantic context in a large corpus of tweets. We experiment on a database of Twitter users and their usernames, and present the first human evaluation for this task. Alignment accuracy approaches human performance at 81{%}, and we show that while lexicographic features are most important, the semantic context of an alias further improves classification accuracy. |
| Tasks | Coreference Resolution, Entity Linking, Sentiment Analysis |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1104/ |
| https://www.aclweb.org/anthology/W17-1104 | |
| PWC | https://paperswithcode.com/paper/aligning-entity-names-with-online-aliases-on |
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LCT-MALTA’s Submission to RepEval 2017 Shared Task
| Title | LCT-MALTA’s Submission to RepEval 2017 Shared Task |
| Authors | Hoa Trong Vu, Thuong-Hai Pham, Xiaoyu Bai, Marc Tanti, Lonneke van der Plas, Albert Gatt |
| Abstract | System using BiLSTM and max pooling. Embedding is enhanced by POS, character and dependency info. |
| Tasks | Natural Language Inference, Sentence Embeddings, Word Embeddings |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-5311/ |
| https://www.aclweb.org/anthology/W17-5311 | |
| PWC | https://paperswithcode.com/paper/lct-maltas-submission-to-repeval-2017-shared |
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Enriching Basque Coreference Resolution System using Semantic Knowledge sources
| Title | Enriching Basque Coreference Resolution System using Semantic Knowledge sources |
| Authors | Ander Soraluze, Olatz Arregi, Xabier Arregi, Arantza D{'\i}az de Ilarraza |
| Abstract | In this paper we present a Basque coreference resolution system enriched with semantic knowledge. An error analysis carried out revealed the deficiencies that the system had in resolving coreference cases in which semantic or world knowledge is needed. We attempt to improve the deficiencies using two semantic knowledge sources, specifically Wikipedia and WordNet. |
| Tasks | Coreference Resolution |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1502/ |
| https://www.aclweb.org/anthology/W17-1502 | |
| PWC | https://paperswithcode.com/paper/enriching-basque-coreference-resolution |
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Multi-source annotation projection of coreference chains: assessing strategies and testing opportunities
| Title | Multi-source annotation projection of coreference chains: assessing strategies and testing opportunities |
| Authors | Yulia Grishina, Manfred Stede |
| Abstract | In this paper, we examine the possibility of using annotation projection from multiple sources for automatically obtaining coreference annotations in the target language. We implement a multi-source annotation projection algorithm and apply it on an English-German-Russian parallel corpus in order to transfer coreference chains from two sources to the target side. Operating in two settings {–} a low-resource and a more linguistically-informed one {–} we show that automatic coreference transfer could benefit from combining information from multiple languages, and assess the quality of both the extraction and the linking of target coreference mentions. |
| Tasks | Coreference Resolution, Named Entity Recognition |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1506/ |
| https://www.aclweb.org/anthology/W17-1506 | |
| PWC | https://paperswithcode.com/paper/multi-source-annotation-projection-of |
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Multivariate Linear Regression of Symptoms-related Tweets for Infectious Gastroenteritis Scale Estimation
| Title | Multivariate Linear Regression of Symptoms-related Tweets for Infectious Gastroenteritis Scale Estimation |
| Authors | Ryo Takeuchi, Hayate Iso, Kaoru Ito, Shoko Wakamiya, Eiji Aramaki |
| Abstract | To date, various Twitter-based event detection systems have been proposed. Most of their targets, however, share common characteristics. They are seasonal or global events such as earthquakes and flu pandemics. In contrast, this study targets unseasonal and local disease events. Our system investigates the frequencies of disease-related words such as {}nausea{''},{}chill{''},and {``}diarrhea{''} and estimates the number of patients using regression of these word frequencies. Experiments conducted using Japanese 47 areas from January 2017 to April 2017 revealed that the detection of small and unseasonal event is extremely difficult (overall performance: 0.13). However, we found that the event scale and the detection performance show high correlation in the specified cases (in the phase of patient increasing or decreasing). The results also suggest that when 150 and more patients appear in a high population area, we can expect that our social sensors detect this outbreak. Based on these results, we can infer that social sensors can reliably detect unseasonal and local disease events under certain conditions, just as they can for seasonal or global events. | |
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| Published | 2017-11-01 |
| URL | https://www.aclweb.org/anthology/W17-5803/ |
| https://www.aclweb.org/anthology/W17-5803 | |
| PWC | https://paperswithcode.com/paper/multivariate-linear-regression-of-symptoms |
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CORBON 2017 Shared Task: Projection-Based Coreference Resolution
| Title | CORBON 2017 Shared Task: Projection-Based Coreference Resolution |
| Authors | Yulia Grishina |
| Abstract | The CORBON 2017 Shared Task, organised as part of the Coreference Resolution Beyond OntoNotes workshop at EACL 2017, presented a new challenge for multilingual coreference resolution: we offer a projection-based setting in which one is supposed to build a coreference resolver for a new language exploiting little or even no knowledge of it, with our languages of interest being German and Russian. We additionally offer a more traditional setting, targeting the development of a multilingual coreference resolver without any restrictions on the resources and methods used. In this paper, we describe the task setting and provide the results of one participant who successfully completed the task, comparing their results to the closely related previous research. Analysing the task setting and the results, we discuss the major challenges and make suggestions on the future directions of coreference evaluation. |
| Tasks | Coreference Resolution, Word Alignment |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1507/ |
| https://www.aclweb.org/anthology/W17-1507 | |
| PWC | https://paperswithcode.com/paper/corbon-2017-shared-task-projection-based |
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A Two-stage Sieve Approach for Quote Attribution
| Title | A Two-stage Sieve Approach for Quote Attribution |
| Authors | Grace Muzny, Michael Fang, Angel Chang, Dan Jurafsky |
| Abstract | We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3. Quote attribution, determining who said what in a given text, is important for tasks like creating dialogue systems, and in newer areas like computational literary studies, where it creates opportunities to analyze novels at scale rather than only a few at a time. We release QuoteLi3, which contains more than 6,000 annotations linking quotes to speaker mentions and quotes to speaker entities, and introduce a new algorithm for quote attribution. Our two-stage algorithm first links quotes to mentions, then mentions to entities. Using two stages encapsulates difficult sub-problems and improves system performance. The modular design allows us to tune for overall performance or higher precision, which is useful for many real-world use cases. Our system achieves an average F-score of 87.5 across three novels, outperforming previous systems, and can be tuned for precision of 90.4 at a recall of 65.1. |
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| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/E17-1044/ |
| https://www.aclweb.org/anthology/E17-1044 | |
| PWC | https://paperswithcode.com/paper/a-two-stage-sieve-approach-for-quote |
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DAG Automata for Meaning Representation
| Title | DAG Automata for Meaning Representation |
| Authors | Frank Drewes |
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| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/W17-3409/ |
| https://www.aclweb.org/anthology/W17-3409 | |
| PWC | https://paperswithcode.com/paper/dag-automata-for-meaning-representation |
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Introducing Structure into Neural Network-Based Semantic Models
| Title | Introducing Structure into Neural Network-Based Semantic Models |
| Authors | Stephen Clark |
| Abstract | |
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| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/W17-3412/ |
| https://www.aclweb.org/anthology/W17-3412 | |
| PWC | https://paperswithcode.com/paper/introducing-structure-into-neural-network |
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