Paper Group NANR 51
Lingmotif: Sentiment Analysis for the Digital Humanities. Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms. Exploring Lavender Tongue from Social Media Texts[In Chinese]. Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitte …
Lingmotif: Sentiment Analysis for the Digital Humanities
| Title | Lingmotif: Sentiment Analysis for the Digital Humanities |
| Authors | Antonio Moreno-Ortiz |
| Abstract | Lingmotif is a lexicon-based, linguistically-motivated, user-friendly, GUI-enabled, multi-platform, Sentiment Analysis desktop application. Lingmotif can perform SA on any type of input texts, regardless of their length and topic. The analysis is based on the identification of sentiment-laden words and phrases contained in the application{'}s rich core lexicons, and employs context rules to account for sentiment shifters. It offers easy-to-interpret visual representations of quantitative data (text polarity, sentiment intensity, sentiment profile), as well as a detailed, qualitative analysis of the text in terms of its sentiment. Lingmotif can also take user-provided plugin lexicons in order to account for domain-specific sentiment expression. Lingmotif currently analyzes English and Spanish texts. |
| Tasks | Sentiment Analysis |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/E17-3019/ |
| https://www.aclweb.org/anthology/E17-3019 | |
| PWC | https://paperswithcode.com/paper/lingmotif-sentiment-analysis-for-the-digital |
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Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms
| Title | Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms |
| Authors | Aleks Wawer, er, Agnieszka Mykowiecka |
| Abstract | This paper compares two approaches to word sense disambiguation using word embeddings trained on unambiguous synonyms. The first is unsupervised method based on computing log probability from sequences of word embedding vectors, taking into account ambiguous word senses and guessing correct sense from context. The second method is supervised. We use a multilayer neural network model to learn a context-sensitive transformation that maps an input vector of ambiguous word into an output vector representing its sense. We evaluate both methods on corpora with manual annotations of word senses from the Polish wordnet (plWordnet). |
| Tasks | Word Embeddings, Word Sense Disambiguation |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1915/ |
| https://www.aclweb.org/anthology/W17-1915 | |
| PWC | https://paperswithcode.com/paper/supervised-and-unsupervised-word-sense |
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Exploring Lavender Tongue from Social Media Texts[In Chinese]
| Title | Exploring Lavender Tongue from Social Media Texts[In Chinese] |
| Authors | Hsiao-Han Wu, Shu-Kai Hsieh |
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| Published | 2017-11-01 |
| URL | https://www.aclweb.org/anthology/O17-1007/ |
| https://www.aclweb.org/anthology/O17-1007 | |
| PWC | https://paperswithcode.com/paper/exploring-lavender-tongue-from-social-media |
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Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter
| Title | Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter |
| Authors | Kristen Johnson, Di Jin, Dan Goldwasser |
| Abstract | Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches. We present a collection of weakly supervised models which harness collective classification to predict the frames used in political discourse on the microblogging platform, Twitter. Our global probabilistic models show that by combining both lexical features of tweets and network-based behavioral features of Twitter, we are able to increase the average, unsupervised F1 score by 21.52 points over a lexical baseline alone. |
| Tasks | |
| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/P17-1069/ |
| https://www.aclweb.org/anthology/P17-1069 | |
| PWC | https://paperswithcode.com/paper/leveraging-behavioral-and-social-information |
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Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
| Title | Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) |
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| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2000/ |
| https://www.aclweb.org/anthology/S17-2000 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-11th-international-2 |
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BioNLP 2017
| Title | BioNLP 2017 |
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| Abstract | |
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| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2300/ |
| https://www.aclweb.org/anthology/W17-2300 | |
| PWC | https://paperswithcode.com/paper/bionlp-2017 |
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Macquarie University at BioASQ 5b – Query-based Summarisation Techniques for Selecting the Ideal Answers
| Title | Macquarie University at BioASQ 5b – Query-based Summarisation Techniques for Selecting the Ideal Answers |
| Authors | Diego Moll{'a} |
| Abstract | Macquarie University{'}s contribution to the BioASQ challenge (Task 5b Phase B) focused on the use of query-based extractive summarisation techniques for the generation of the ideal answers. Four runs were submitted, with approaches ranging from a trivial system that selected the first $n$ snippets, to the use of deep learning approaches under a regression framework. Our experiments and the ROUGE results of the five test batches of BioASQ indicate surprisingly good results for the trivial approach. Overall, most of our runs on the first three test batches achieved the best ROUGE-SU4 results in the challenge. |
| Tasks | Question Answering |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2308/ |
| https://www.aclweb.org/anthology/W17-2308 | |
| PWC | https://paperswithcode.com/paper/macquarie-university-at-bioasq-5b-a-query |
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A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC
| Title | A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC |
| Authors | Zhuoxuan Jiang, Shanshan Feng, Gao Cong, Chunyan Miao, Xiaoming Li |
| Abstract | Recent years have witnessed the proliferation of Massive Open Online Courses (MOOCs). With massive learners being offered MOOCs, there is a demand that the forum contents within MOOCs need to be classified in order to facilitate both learners and instructors. Therefore we investigate a significant application, which is to associate forum threads to subtitles of video clips. This task can be regarded as a document ranking problem, and the key is how to learn a distinguishable text representation from word sequences and learners{'} behavior sequences. In this paper, we propose a novel cascade model, which can capture both the latent semantics and latent similarity by modeling MOOC data. Experimental results on two real-world datasets demonstrate that our textual representation outperforms state-of-the-art unsupervised counterparts for the application. |
| Tasks | Document Ranking, Sentiment Analysis, Word Embeddings |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/D17-1293/ |
| https://www.aclweb.org/anthology/D17-1293 | |
| PWC | https://paperswithcode.com/paper/a-novel-cascade-model-for-learning-latent |
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Hybrid Collaborative Movie Recommender System Using Clustering and Bat Optimization
| Title | Hybrid Collaborative Movie Recommender System Using Clustering and Bat Optimization |
| Authors | Vimala Vellaichamy, Vivekanandan Kalimuthu |
| Abstract | A Recommender system (RS) is an information filtering software that helps users with a personalized manner to recommend online products to Users and give suggestions about the products that he or she might like. In e-commerce, collaborative Movie recommender system assist users to select their favorite movies based on their similar neighbor’s movie ratings. However due to data sparsity and scalability problems, neighborhood selection is more challenging with the rapid increasing number of users and movies. In this paper, a hybrid Collaborative Movie Recommender system is proposed that combines Fuzzy C Means clustering (FCM) with Bat optimization to reduce the scalability problem and enhance the clustering which improves recommendation quality. Fuzzy c means clustering is used to cluster the users into different groups. Bat Algorithm is used to obtain the initial position of clusters. Lastly, the proposed system creates movie recommendations for target users. The proposed system was evaluated over Movie Lens dataset. Experiment results obtained show that the proposed Algorithm can yield better recommendation results compared to other techniques in terms of Mean Absolute Error (MAE), precision and Recall. |
| Tasks | Recommendation Systems |
| Published | 2017-06-04 |
| URL | http://scholar.google.co.in/scholar?q=Movie+Recommender+System+Using+Fuzzy-cmeans+Clustering&hl=en&as_sdt=0&as_vis=1&oi=scholart |
| http://www.inass.org/2017/2017103105.pdf | |
| PWC | https://paperswithcode.com/paper/hybrid-collaborative-movie-recommender-system |
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Inherent Biases of Recurrent Neural Networks for Phonological Assimilation and Dissimilation
| Title | Inherent Biases of Recurrent Neural Networks for Phonological Assimilation and Dissimilation |
| Authors | Am Doucette, a |
| Abstract | A recurrent neural network model of phonological pattern learning is proposed. The model is a relatively simple neural network with one recurrent layer, and displays biases in learning that mimic observed biases in human learning. Single-feature patterns are learned faster than two-feature patterns, and vowel or consonant-only patterns are learned faster than patterns involving vowels and consonants, mimicking the results of laboratory learning experiments. In non-recurrent models, capturing these biases requires the use of alpha features or some other representation of repeated features, but with a recurrent neural network, these elaborations are not necessary. |
| Tasks | |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-0705/ |
| https://www.aclweb.org/anthology/W17-0705 | |
| PWC | https://paperswithcode.com/paper/inherent-biases-of-recurrent-neural-networks-1 |
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Adaptive Active Hypothesis Testing under Limited Information
| Title | Adaptive Active Hypothesis Testing under Limited Information |
| Authors | Fabio Cecchi, Nidhi Hegde |
| Abstract | We consider the problem of active sequential hypothesis testing where a Bayesian decision maker must infer the true hypothesis from a set of hypotheses. The decision maker may choose for a set of actions, where the outcome of an action is corrupted by independent noise. In this paper we consider a special case where the decision maker has limited knowledge about the distribution of observations for each action, in that only a binary value is observed. Our objective is to infer the true hypothesis with low error, while minimizing the number of action sampled. Our main results include the derivation of a lower bound on sample size for our system under limited knowledge and the design of an active learning policy that matches this lower bound and outperforms similar known algorithms. |
| Tasks | Active Learning |
| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/6992-adaptive-active-hypothesis-testing-under-limited-information |
| http://papers.nips.cc/paper/6992-adaptive-active-hypothesis-testing-under-limited-information.pdf | |
| PWC | https://paperswithcode.com/paper/adaptive-active-hypothesis-testing-under |
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GEC into the future: Where are we going and how do we get there?
| Title | GEC into the future: Where are we going and how do we get there? |
| Authors | Keisuke Sakaguchi, Courtney Napoles, Joel Tetreault |
| Abstract | The field of grammatical error correction (GEC) has made tremendous bounds in the last ten years, but new questions and obstacles are revealing themselves. In this position paper, we discuss the issues that need to be addressed and provide recommendations for the field to continue to make progress, and propose a new shared task. We invite suggestions and critiques from the audience to make the new shared task a community-driven venture. |
| Tasks | Grammatical Error Correction, Machine Translation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-5019/ |
| https://www.aclweb.org/anthology/W17-5019 | |
| PWC | https://paperswithcode.com/paper/gec-into-the-future-where-are-we-going-and |
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Beyond On-hold Messages: Conversational Time-buying in Task-oriented Dialogue
| Title | Beyond On-hold Messages: Conversational Time-buying in Task-oriented Dialogue |
| Authors | Soledad L{'o}pez Gambino, Sina Zarrie{\ss}, David Schlangen |
| Abstract | A common convention in graphical user interfaces is to indicate a {}wait state{''}, for example while a program is preparing a response, through a changed cursor state or a progress bar. What should the analogue be in a spoken conversational system? To address this question, we set up an experiment in which a human information provider (IP) was given their information only in a delayed and incremental manner, which systematically created situations where the IP had the turn but could not provide task-related information. Our data analysis shows that 1) IPs bridge the gap until they can provide information by re-purposing a whole variety of task- and grounding-related communicative actions (e.g. echoing the user{'}s request, signaling understanding, asserting partially relevant information), rather than being silent or explicitly asking for time (e.g. {}please wait{''}), and that 2) IPs combined these actions productively to ensure an ongoing conversation. These results, we argue, indicate that natural conversational interfaces should also be able to manage their time flexibly using a variety of conversational resources. |
| Tasks | |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-5529/ |
| https://www.aclweb.org/anthology/W17-5529 | |
| PWC | https://paperswithcode.com/paper/beyond-on-hold-messages-conversational-time |
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BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora
| Title | BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora |
| Authors | Francis Gr{'e}goire, Philippe Langlais |
| Abstract | This paper describes our participation in BUCC 2017 shared task: identifying parallel sentences in comparable corpora. Our goal is to leverage continuous vector representations and distributional semantics with a minimal use of external preprocessing and postprocessing tools. We report experiments that were conducted after transmitting our results. |
| Tasks | Feature Engineering, Language Modelling, Machine Translation, Semantic Textual Similarity, Word Embeddings |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2509/ |
| https://www.aclweb.org/anthology/W17-2509 | |
| PWC | https://paperswithcode.com/paper/bucc-2017-shared-task-a-first-attempt-toward |
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Inferring Case Systems from IGT: Enriching the Enrichment
| Title | Inferring Case Systems from IGT: Enriching the Enrichment |
| Authors | Kristen Howell, Emily M. Bender, Michel Lockwood, Fei Xia, Olga Zamaraeva |
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| Published | 2017-03-01 |
| URL | https://www.aclweb.org/anthology/W17-0110/ |
| https://www.aclweb.org/anthology/W17-0110 | |
| PWC | https://paperswithcode.com/paper/inferring-case-systems-from-igt-enriching-the |
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