Paper Group NANR 144
DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations. Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016). Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016). CATaLog Online: Porting a Post-editing Tool to the W …
DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations
| Title | DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations |
| Authors | Honglei Li, Jianhai Zhang, Jian Wang, Hongfei Lin, Zhihao Yang |
| Abstract | |
| Tasks | Feature Engineering, Relation Extraction |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-3012/ |
| https://www.aclweb.org/anthology/W16-3012 | |
| PWC | https://paperswithcode.com/paper/dutir-in-bionlp-st-2016-utilizing |
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Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)
| Title | Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016) |
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| Abstract | |
| Tasks | Question Answering |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4400/ |
| https://www.aclweb.org/anthology/W16-4400 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-open-knowledge-base-and |
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Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)
| Title | Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016) |
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| Abstract | |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4700/ |
| https://www.aclweb.org/anthology/W16-4700 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-5th-international-workshop |
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CATaLog Online: Porting a Post-editing Tool to the Web
| Title | CATaLog Online: Porting a Post-editing Tool to the Web |
| Authors | Santanu Pal, Marcos Zampieri, Sudip Kumar Naskar, Tapas Nayak, Mihaela Vela, Josef van Genabith |
| Abstract | This paper presents CATaLog online, a new web-based MT and TM post-editing tool. CATaLog online is a freeware software that can be used through a web browser and it requires only a simple registration. The tool features a number of editing and log functions similar to the desktop version of CATaLog enhanced with several new features that we describe in detail in this paper. CATaLog online is designed to allow users to post-edit both translation memory segments as well as machine translation output. The tool provides a complete set of log information currently not available in most commercial CAT tools. Log information can be used both for project management purposes as well as for the study of the translation process and translator{'}s productivity. |
| Tasks | Machine Translation |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1095/ |
| https://www.aclweb.org/anthology/L16-1095 | |
| PWC | https://paperswithcode.com/paper/catalog-online-porting-a-post-editing-tool-to |
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Typology of Adjectives Benchmark for Compositional Distributional Models
| Title | Typology of Adjectives Benchmark for Compositional Distributional Models |
| Authors | Daria Ryzhova, Maria Kyuseva, Denis Paperno |
| Abstract | In this paper we present a novel application of compositional distributional semantic models (CDSMs): prediction of lexical typology. The paper introduces the notion of typological closeness, which is a novel rigorous formalization of semantic similarity based on comparison of multilingual data. Starting from the Moscow Database of Qualitative Features for adjective typology, we create four datasets of typological closeness, on which we test a range of distributional semantic models. We show that, on the one hand, vector representations of phrases based on data from one language can be used to predict how words within the phrase translate into different languages, and, on the other hand, that typological data can serve as a semantic benchmark for distributional models. We find that compositional distributional models, especially parametric ones, perform way above non-compositional alternatives on the task. |
| Tasks | Semantic Similarity, Semantic Textual Similarity |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1199/ |
| https://www.aclweb.org/anthology/L16-1199 | |
| PWC | https://paperswithcode.com/paper/typology-of-adjectives-benchmark-for |
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Deep Submodular Functions: Definitions and Learning
| Title | Deep Submodular Functions: Definitions and Learning |
| Authors | Brian W. Dolhansky, Jeff A. Bilmes |
| Abstract | We propose and study a new class of submodular functions called deep submodular functions (DSFs). We define DSFs and situate them within the broader context of classes of submodular functions in relationship both to various matroid ranks and sums of concave composed with modular functions (SCMs). Notably, we find that DSFs constitute a strictly broader class than SCMs, thus motivating their use, but that they do not comprise all submodular functions. Interestingly, some DSFs can be seen as special cases of certain deep neural networks (DNNs), hence the name. Finally, we provide a method to learn DSFs in a max-margin framework, and offer preliminary results applying this both to synthetic and real-world data instances. |
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| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6361-deep-submodular-functions-definitions-and-learning |
| http://papers.nips.cc/paper/6361-deep-submodular-functions-definitions-and-learning.pdf | |
| PWC | https://paperswithcode.com/paper/deep-submodular-functions-definitions-and |
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A Proposition-Based Abstractive Summariser
| Title | A Proposition-Based Abstractive Summariser |
| Authors | Yimai Fang, Haoyue Zhu, Ewa Muszy{'n}ska, Alex Kuhnle, er, Simone Teufel |
| Abstract | Abstractive summarisation is not yet common amongst today{'}s deployed and research systems. Most existing systems either extract sentences or compress individual sentences. In this paper, we present a summariser that works by a different paradigm. It is a further development of an existing summariser that has an incremental, proposition-based content selection process but lacks a natural language (NL) generator for the final output. Using an NL generator, we can now produce the summary text to directly reflect the selected propositions. Our evaluation compares textual quality of our system to the earlier preliminary output method, and also uses ROUGE to compare to various summarisers that use the traditional method of sentence extraction, followed by compression. Our results suggest that cutting out the middle-man of sentence extraction can lead to better abstractive summaries. |
| Tasks | Language Modelling, Sentence Compression |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1055/ |
| https://www.aclweb.org/anthology/C16-1055 | |
| PWC | https://paperswithcode.com/paper/a-proposition-based-abstractive-summariser |
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Concepticon: A Resource for the Linking of Concept Lists
| Title | Concepticon: A Resource for the Linking of Concept Lists |
| Authors | Johann-Mattis List, Michael Cysouw, Robert Forkel |
| Abstract | We present an attempt to link the large amount of different concept lists which are used in the linguistic literature, ranging from Swadesh lists in historical linguistics to naming tests in clinical studies and psycholinguistics. This resource, our Concepticon, links 30 222 concept labels from 160 conceptlists to 2495 concept sets. Each concept set is given a unique identifier, a unique label, and a human-readable definition. Concept sets are further structured by defining different relations between the concepts. The resource can be used for various purposes. Serving as a rich reference for new and existing databases in diachronic and synchronic linguistics, it allows researchers a quick access to studies on semantic change, cross-linguistic polysemies, and semantic associations. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1379/ |
| https://www.aclweb.org/anthology/L16-1379 | |
| PWC | https://paperswithcode.com/paper/concepticon-a-resource-for-the-linking-of |
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Punctuation Prediction for Unsegmented Transcript Based on Word Vector
| Title | Punctuation Prediction for Unsegmented Transcript Based on Word Vector |
| Authors | Xiaoyin Che, Cheng Wang, Haojin Yang, Christoph Meinel |
| Abstract | In this paper we propose an approach to predict punctuation marks for unsegmented speech transcript. The approach is purely lexical, with pre-trained Word Vectors as the only input. A training model of Deep Neural Network (DNN) or Convolutional Neural Network (CNN) is applied to classify whether a punctuation mark should be inserted after the third word of a 5-words sequence and which kind of punctuation mark the inserted one should be. TED talks within IWSLT dataset are used in both training and evaluation phases. The proposed approach shows its effectiveness by achieving better result than the state-of-the-art lexical solution which works with same type of data, especially when predicting puncuation position only. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1103/ |
| https://www.aclweb.org/anthology/L16-1103 | |
| PWC | https://paperswithcode.com/paper/punctuation-prediction-for-unsegmented |
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Analysing Constraint Grammars with a SAT-solver
| Title | Analysing Constraint Grammars with a SAT-solver |
| Authors | Inari Listenmaa, Koen Claessen |
| Abstract | We describe a method for analysing Constraint Grammars (CG) that can detect internal conflicts and redundancies in a given grammar, without the need for a corpus. The aim is for grammar writers to be able to automatically diagnose, and then manually improve their grammars. Our method works by translating the given grammar into logical constraints that are analysed by a SAT-solver. We have evaluated our analysis on a number of non-trivial grammars and found inconsistencies. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1111/ |
| https://www.aclweb.org/anthology/L16-1111 | |
| PWC | https://paperswithcode.com/paper/analysing-constraint-grammars-with-a-sat |
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A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets
| Title | A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets |
| Authors | Javid Ebrahimi, Dejing Dou, Daniel Lowd |
| Abstract | Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining. We propose a probabilistic approach to stance classification in tweets, which models stance, target of stance, and sentiment of tweet, jointly. Instead of simply conjoining the sentiment or target variables as extra variables to the feature space, we use a novel formulation to incorporate three-way interactions among sentiment-stance-input variables and three-way interactions among target-stance-input variables. The proposed specification intuitively aims to discriminate sentiment features from target features for stance classification. In addition, regularizing a single stance classifier, which handles all targets, acts as a soft weight-sharing among them. We demonstrate that discriminative training of this model achieves the state-of-the-art results in supervised stance classification, and its generative training obtains competitive results in the weakly supervised setting. |
| Tasks | Argument Mining, Opinion Mining, Sentiment Analysis, Stance Detection, Subjectivity Analysis |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1250/ |
| https://www.aclweb.org/anthology/C16-1250 | |
| PWC | https://paperswithcode.com/paper/a-joint-sentiment-target-stance-model-for |
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A subtree-based factorization of dependency parsing
| Title | A subtree-based factorization of dependency parsing |
| Authors | Qiuye Zhao, Qun Liu |
| Abstract | We propose a dependency parsing pipeline, in which the parsing of long-distance projections and localized dependencies are explicitly decomposed at the input level. A chosen baseline dependency parsing model performs only on {`}carved{'} sequences at the second stage, which are transformed from coarse constituent parsing outputs at the first stage. When k-best constituent parsing outputs are kept, a third-stage is required to search for an optimal combination of the overlapped dependency subtrees. In this sense, our dependency model is subtree-factored. We explore alternative approaches for scoring subtrees, including feature-based models as well as continuous representations. The search for optimal subset to combine is formulated as an ILP problem. This framework especially benefits the models poor on long sentences, generally improving baselines by 0.75-1.28 (UAS) on English, achieving comparable performance with high-order models but faster. For Chinese, the most notable increase is as high as 3.63 (UAS) when the proposed framework is applied to first-order parsing models. | |
| Tasks | Dependency Parsing |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1057/ |
| https://www.aclweb.org/anthology/C16-1057 | |
| PWC | https://paperswithcode.com/paper/a-subtree-based-factorization-of-dependency |
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A Bayesian model for joint word alignment and part-of-speech transfer
| Title | A Bayesian model for joint word alignment and part-of-speech transfer |
| Authors | Robert {"O}stling |
| Abstract | Current methods for word alignment require considerable amounts of parallel text to deliver accurate results, a requirement which is met only for a small minority of the world{'}s approximately 7,000 languages. We show that by jointly performing word alignment and annotation transfer in a novel Bayesian model, alignment accuracy can be improved for language pairs where annotations are available for only one of the languages{—}a finding which could facilitate the study and processing of a vast number of low-resource languages. We also present an evaluation where our method is used to perform single-source and multi-source part-of-speech transfer with 22 translations of the same text in four different languages. This allows us to quantify the considerable variation in accuracy depending on the specific source text(s) used, even with different translations into the same language. |
| Tasks | Machine Translation, Word Alignment, Word Sense Disambiguation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1060/ |
| https://www.aclweb.org/anthology/C16-1060 | |
| PWC | https://paperswithcode.com/paper/a-bayesian-model-for-joint-word-alignment-and |
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An Alternate View on Strong Lexicalization in TAG
| Title | An Alternate View on Strong Lexicalization in TAG |
| Authors | Aniello De Santo, Al{"e}na Aks{"e}nova, Thomas Graf |
| Abstract | |
| Tasks | |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-3310/ |
| https://www.aclweb.org/anthology/W16-3310 | |
| PWC | https://paperswithcode.com/paper/an-alternate-view-on-strong-lexicalization-in |
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Probabilistic Prototype Model for Serendipitous Property Mining
| Title | Probabilistic Prototype Model for Serendipitous Property Mining |
| Authors | Taesung Lee, Seung-won Hwang, Zhongyuan Wang |
| Abstract | Besides providing the relevant information, amusing users has been an important role of the web. Many web sites provide serendipitous (unexpected but relevant) information to draw user traffic. In this paper, we study the representative scenario of mining an amusing quiz. An existing approach leverages a knowledge base to mine an unexpected property then find quiz questions on such property, based on prototype theory in cognitive science. However, existing deterministic model is vulnerable to noise in the knowledge base. Therefore, we instead propose to leverage probabilistic approach to build a prototype that can overcome noise. Our extensive empirical study shows that our approach not only significantly outperforms baselines by 0.06 in accuracy, and 0.11 in serendipity but also shows higher relevance than the traditional relevance-pursuing baseline using TF-IDF. |
| Tasks | Question Generation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1064/ |
| https://www.aclweb.org/anthology/C16-1064 | |
| PWC | https://paperswithcode.com/paper/probabilistic-prototype-model-for |
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