Paper Group NANR 50
Hybrid Morphological Segmentation for Phrase-Based Machine Translation. Language Transfer Learning for Supervised Lexical Substitution. Automatically Generated Affective Norms of Abstractness, Arousal, Imageability and Valence for 350 000 German Lemmas. Distinguishing Literal and Non-Literal Usage of German Particle Verbs. A Framework for Cross-lin …
Hybrid Morphological Segmentation for Phrase-Based Machine Translation
| Title | Hybrid Morphological Segmentation for Phrase-Based Machine Translation |
| Authors | Stig-Arne Gr{"o}nroos, Sami Virpioja, Mikko Kurimo |
| Abstract | |
| Tasks | Language Modelling, Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2312/ |
| https://www.aclweb.org/anthology/W16-2312 | |
| PWC | https://paperswithcode.com/paper/hybrid-morphological-segmentation-for-phrase |
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Language Transfer Learning for Supervised Lexical Substitution
| Title | Language Transfer Learning for Supervised Lexical Substitution |
| Authors | Gerold Hintz, Chris Biemann |
| Abstract | |
| Tasks | Semantic Textual Similarity, Text Simplification, Transfer Learning, Word Embeddings, Word Sense Disambiguation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1012/ |
| https://www.aclweb.org/anthology/P16-1012 | |
| PWC | https://paperswithcode.com/paper/language-transfer-learning-for-supervised |
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Automatically Generated Affective Norms of Abstractness, Arousal, Imageability and Valence for 350 000 German Lemmas
| Title | Automatically Generated Affective Norms of Abstractness, Arousal, Imageability and Valence for 350 000 German Lemmas |
| Authors | Maximilian K{"o}per, Sabine Schulte im Walde |
| Abstract | This paper presents a collection of 350,000 German lemmatised words, rated on four psycholinguistic affective attributes. All ratings were obtained via a supervised learning algorithm that can automatically calculate a numerical rating of a word. We applied this algorithm to abstractness, arousal, imageability and valence. Comparison with human ratings reveals high correlation across all rating types. The full resource is publically available at: http://www.ims.uni-stuttgart.de/data/affective{_}norms/ |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1413/ |
| https://www.aclweb.org/anthology/L16-1413 | |
| PWC | https://paperswithcode.com/paper/automatically-generated-affective-norms-of |
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Distinguishing Literal and Non-Literal Usage of German Particle Verbs
| Title | Distinguishing Literal and Non-Literal Usage of German Particle Verbs |
| Authors | Maximilian K{"o}per, Sabine Schulte im Walde |
| Abstract | |
| Tasks | Language Identification, Machine Translation, Sentiment Analysis |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1039/ |
| https://www.aclweb.org/anthology/N16-1039 | |
| PWC | https://paperswithcode.com/paper/distinguishing-literal-and-non-literal-usage |
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A Framework for Cross-lingual/Node-wise Alignment of Lexical-Semantic Resources
| Title | A Framework for Cross-lingual/Node-wise Alignment of Lexical-Semantic Resources |
| Authors | Yoshihiko Hayashi |
| Abstract | Given lexical-semantic resources in different languages, it is useful to establish cross-lingual correspondences, preferably with semantic relation labels, between the concept nodes in these resources. This paper presents a framework for enabling a cross-lingual/node-wise alignment of lexical-semantic resources, where cross-lingual correspondence candidates are first discovered and ranked, and then classified by a succeeding module. Indeed, we propose that a two-tier classifier configuration is feasible for the second module: the first classifier filters out possibly irrelevant correspondence candidates and the second classifier assigns a relatively fine-grained semantic relation label to each of the surviving candidates. The results of Japanese-to-English alignment experiments using EDR Electronic Dictionary and Princeton WordNet are described to exemplify the validity of the proposal. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1415/ |
| https://www.aclweb.org/anthology/L16-1415 | |
| PWC | https://paperswithcode.com/paper/a-framework-for-cross-lingualnode-wise |
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EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis
| Title | EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis |
| Authors | Jasy Suet Yan Liew, Howard R. Turtle, Elizabeth D. Liddy |
| Abstract | This paper describes EmoTweet-28, a carefully curated corpus of 15,553 tweets annotated with 28 emotion categories for the purpose of training and evaluating machine learning models for emotion classification. EmoTweet-28 is, to date, the largest tweet corpus annotated with fine-grained emotion categories. The corpus contains annotations for four facets of emotion: valence, arousal, emotion category and emotion cues. We first used small-scale content analysis to inductively identify a set of emotion categories that characterize the emotions expressed in microblog text. We then expanded the size of the corpus using crowdsourcing. The corpus encompasses a variety of examples including explicit and implicit expressions of emotions as well as tweets containing multiple emotions. EmoTweet-28 represents an important resource to advance the development and evaluation of more emotion-sensitive systems. |
| Tasks | Emotion Classification, Sentiment Analysis |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1183/ |
| https://www.aclweb.org/anthology/L16-1183 | |
| PWC | https://paperswithcode.com/paper/emotweet-28-a-fine-grained-emotion-corpus-for |
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Addressing Annotation Complexity: The Case of Annotating Ideological Perspective in Egyptian Social Media
| Title | Addressing Annotation Complexity: The Case of Annotating Ideological Perspective in Egyptian Social Media |
| Authors | Heba Elfardy, Mona Diab |
| Abstract | |
| Tasks | Recommendation Systems |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-1710/ |
| https://www.aclweb.org/anthology/W16-1710 | |
| PWC | https://paperswithcode.com/paper/addressing-annotation-complexity-the-case-of |
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| Framework | |
Convolutional aggregation of local evidence for large pose face alignment
| Title | Convolutional aggregation of local evidence for large pose face alignment |
| Authors | Adrian Bulat, Georgios Tzimiropoulos |
| Abstract | Methods for unconstrained face alignment must satisfy two requirements: they must not rely on accurate initialisation/face detection and they should perform equally well for the whole spectrum of facial poses. To the best of our knowledge, there are no methods meeting these requirements to satisfactory extent, and in this paper, we propose Convolutional Aggregation of Local Evidence (CALE), a Convolutional Neural Network (CNN) architecture particularly designed for addressing both of them. In particular, to remove the requirement for accurate face detection, our system firstly performs facial part detection, providing confidence scores for the location of each of the facial landmarks (local evidence). Next, these score maps along with early CNN features are aggregated by our system through joint regression in order to refine the landmarks’ location. Besides playing the role of a graphical model, CNN regression is a key feature of our system, guiding the network to rely on context for predicting the location of occluded landmarks, typically encountered in very large poses. The whole system is trained end-to-end with intermediate supervision. When applied to AFLW-PIFA, the most challenging human face alignment test set to date, our method provides more than 50% gain in localisation accuracy when compared to other recently published methods for large pose face alignment. Going beyond human faces, we also demonstrate that CALE is effective in dealing with very large changes in shape and appearance, typically encountered in animal faces. |
| Tasks | Face Alignment, Face Detection |
| Published | 2016-09-21 |
| URL | https://www.adrianbulat.com/downloads/BMVC16/cale_bmvc16.pdf |
| https://www.adrianbulat.com/downloads/BMVC16/cale_bmvc16.pdf | |
| PWC | https://paperswithcode.com/paper/convolutional-aggregation-of-local-evidence |
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Semantic Layer of the Valence Dictionary of Polish Walenty
| Title | Semantic Layer of the Valence Dictionary of Polish Walenty |
| Authors | El{.z}bieta Hajnicz, Anna Andrzejczuk, Tomasz Bartosiak |
| Abstract | This article presents the semantic layer of Walenty―a new valence dictionary of Polish predicates, with a number of novel features, as compared to other such dictionaries. The dictionary contains two layers, syntactic and semantic. The syntactic layer describes syntactic and morphosyntactic constraints predicates put on their dependants. In particular, it includes a comprehensive and powerful phraseological component. The semantic layer shows how predicates and their arguments are involved in a described situation in an utterance. These two layers are connected, representing how semantic arguments can be realised on the surface. Each syntactic schema and each semantic frame are illustrated by at least one exemplary sentence attested in linguistic reality. The semantic layer consists of semantic frames represented as lists of pairs and connected with PlWordNet lexical units. Semantic roles have a two-level representation (basic roles are provided with an attribute) enabling representation of arguments in a flexible way. Selectional preferences are based on PlWordNet structure as well. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1418/ |
| https://www.aclweb.org/anthology/L16-1418 | |
| PWC | https://paperswithcode.com/paper/semantic-layer-of-the-valence-dictionary-of |
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| Framework | |
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities
| Title | One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities |
| Authors | Michalis Titsias Rc Aueb |
| Abstract | The softmax representation of probabilities for categorical variables plays a prominent role in modern machine learning with numerous applications in areas such as large scale classification, neural language modeling and recommendation systems. However, softmax estimation is very expensive for large scale inference because of the high cost associated with computing the normalizing constant. Here, we introduce an efficient approximation to softmax probabilities which takes the form of a rigorous lower bound on the exact probability. This bound is expressed as a product over pairwise probabilities and it leads to scalable estimation based on stochastic optimization. It allows us to perform doubly stochastic estimation by subsampling both training instances and class labels. We show that the new bound has interesting theoretical properties and we demonstrate its use in classification problems. |
| Tasks | Language Modelling, Recommendation Systems, Stochastic Optimization |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6468-one-vs-each-approximation-to-softmax-for-scalable-estimation-of-probabilities |
| http://papers.nips.cc/paper/6468-one-vs-each-approximation-to-softmax-for-scalable-estimation-of-probabilities.pdf | |
| PWC | https://paperswithcode.com/paper/one-vs-each-approximation-to-softmax-for |
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Self-Reflective Sentiment Analysis
| Title | Self-Reflective Sentiment Analysis |
| Authors | Benjamin Shickel, Martin Heesacker, Sherry Benton, Ashkan Ebadi, Paul Nickerson, Parisa Rashidi |
| Abstract | |
| Tasks | Sentiment Analysis, Word Embeddings |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-0303/ |
| https://www.aclweb.org/anthology/W16-0303 | |
| PWC | https://paperswithcode.com/paper/self-reflective-sentiment-analysis |
| Repo | |
| Framework | |
Linguistic Understanding of Complaints and Praises in User Reviews
| Title | Linguistic Understanding of Complaints and Praises in User Reviews |
| Authors | Guangyu Zhou, Kavita Ganesan |
| Abstract | |
| Tasks | Sentiment Analysis |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-0418/ |
| https://www.aclweb.org/anthology/W16-0418 | |
| PWC | https://paperswithcode.com/paper/linguistic-understanding-of-complaints-and |
| Repo | |
| Framework | |
Enriching a Portuguese WordNet using Synonyms from a Monolingual Dictionary
| Title | Enriching a Portuguese WordNet using Synonyms from a Monolingual Dictionary |
| Authors | Alberto Sim{~o}es, Xavier G{'o}mez Guinovart, Jos{'e} Jo{~a}o Almeida |
| Abstract | In this article we present an exploratory approach to enrich a WordNet-like lexical ontology with the synonyms present in a standard monolingual Portuguese dictionary. The dictionary was converted from PDF into XML and senses were automatically identified and annotated. This allowed us to extract them, independently of definitions, and to create sets of synonyms (synsets). These synsets were then aligned with WordNet synsets, both in the same language (Portuguese) and projecting the Portuguese terms into English, Spanish and Galician. This process allowed both the addition of new term variants to existing synsets, as to create new synsets for Portuguese. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1426/ |
| https://www.aclweb.org/anthology/L16-1426 | |
| PWC | https://paperswithcode.com/paper/enriching-a-portuguese-wordnet-using-synonyms |
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| Framework | |
VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval
| Title | VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval |
| Authors | Tommaso Caselli, Roser Morante |
| Abstract | |
| Tasks | Domain Adaptation, Natural Language Inference, Question Answering, Relation Classification, Text Summarization |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1193/ |
| https://www.aclweb.org/anthology/S16-1193 | |
| PWC | https://paperswithcode.com/paper/vuacltl-at-semeval-2016-task-12-a-crf |
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Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention
| Title | Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention |
| Authors | Haoran Huang, Qi Zhang, Yeyun Gong, Xuanjing Huang |
| Abstract | On microblogging services, people usually use hashtags to mark microblogs, which have a specific theme or content, making them easier for users to find. Hence, how to automatically recommend hashtags for microblogs has received much attention in recent years. Previous deep neural network-based hashtag recommendation approaches converted the task into a multi-class classification problem. However, most of these methods only took the microblog itself into consideration. Motivated by the intuition that the history of users should impact the recommendation procedure, in this work, we extend end-to-end memory networks to perform this task. We incorporate the histories of users into the external memory and introduce a hierarchical attention mechanism to select more appropriate histories. To train and evaluate the proposed method, we also construct a dataset based on microblogs collected from Twitter. Experimental results demonstrate that the proposed methods can significantly outperform state-of-the-art methods. By incorporating the hierarchical attention mechanism, the relative improvement in the proposed method over the state-of-the-art method is around 67.9{%} in the F1-score. |
| Tasks | Machine Translation, Sentiment Analysis |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1090/ |
| https://www.aclweb.org/anthology/C16-1090 | |
| PWC | https://paperswithcode.com/paper/hashtag-recommendation-using-end-to-end |
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