Paper Group NANR 41
Hashtag Processing for Enhanced Clustering of Tweets. ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain. Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces. TextFlow: A Text Similarity Measure based on Continuous Sequences. A Com …
Hashtag Processing for Enhanced Clustering of Tweets
| Title | Hashtag Processing for Enhanced Clustering of Tweets |
| Authors | Dagmar Gromann, Thierry Declerck |
| Abstract | Rich data provided by tweets have beenanalyzed, clustered, and explored in a variety of studies. Typically those studies focus on named entity recognition, entity linking, and entity disambiguation or clustering. Tweets and hashtags are generally analyzed on sentential or word level but not on a compositional level of concatenated words. We propose an approach for a closer analysis of compounds in hashtags, and in the long run also of other types of text sequences in tweets, in order to enhance the clustering of such text documents. Hashtags have been used before as primary topic indicators to cluster tweets, however, their segmentation and its effect on clustering results have not been investigated to the best of our knowledge. Our results with a standard dataset from the Text REtrieval Conference (TREC) show that segmented and harmonized hashtags positively impact effective clustering. |
| Tasks | Entity Disambiguation, Entity Linking, Named Entity Recognition |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/R17-1038/ |
| https://doi.org/10.26615/978-954-452-049-6_038 | |
| PWC | https://paperswithcode.com/paper/hashtag-processing-for-enhanced-clustering-of |
| Repo | |
| Framework | |
ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain
| Title | ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain |
| Authors | Mengxiao Jiang, Man Lan, Yuanbin Wu |
| Abstract | This paper describes our systems submitted to the Fine-Grained Sentiment Analysis on Financial Microblogs and News task (i.e., Task 5) in SemEval-2017. This task includes two subtasks in microblogs and news headline domain respectively. To settle this problem, we extract four types of effective features, including linguistic features, sentiment lexicon features, domain-specific features and word embedding features. Then we employ these features to construct models by using ensemble regression algorithms. Our submissions rank 1st and rank 5th in subtask 1 and subtask 2 respectively. |
| Tasks | Feature Engineering, Sentiment Analysis |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2152/ |
| https://www.aclweb.org/anthology/S17-2152 | |
| PWC | https://paperswithcode.com/paper/ecnu-at-semeval-2017-task-5-an-ensemble-of |
| Repo | |
| Framework | |
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces
| Title | Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces |
| Authors | Daniel Milstein, Jason Pacheco, Leigh Hochberg, John D. Simeral, Beata Jarosiewicz, Erik Sudderth |
| Abstract | Intracortical brain-computer interfaces (iBCIs) have allowed people with tetraplegia to control a computer cursor by imagining the movement of their paralyzed arm or hand. State-of-the-art decoders deployed in human iBCIs are derived from a Kalman filter that assumes Markov dynamics on the angle of intended movement, and a unimodal dependence on intended angle for each channel of neural activity. Due to errors made in the decoding of noisy neural data, as a user attempts to move the cursor to a goal, the angle between cursor and goal positions may change rapidly. We propose a dynamic Bayesian network that includes the on-screen goal position as part of its latent state, and thus allows the person’s intended angle of movement to be aggregated over a much longer history of neural activity. This multiscale model explicitly captures the relationship between instantaneous angles of motion and long-term goals, and incorporates semi-Markov dynamics for motion trajectories. We also introduce a multimodal likelihood model for recordings of neural populations which can be rapidly calibrated for clinical applications. In offline experiments with recorded neural data, we demonstrate significantly improved prediction of motion directions compared to the Kalman filter. We derive an efficient online inference algorithm, enabling a clinical trial participant with tetraplegia to control a computer cursor with neural activity in real time. The observed kinematics of cursor movement are objectively straighter and smoother than prior iBCI decoding models without loss of responsiveness. |
| Tasks | |
| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/6688-multiscale-semi-markov-dynamics-for-intracortical-brain-computer-interfaces |
| http://papers.nips.cc/paper/6688-multiscale-semi-markov-dynamics-for-intracortical-brain-computer-interfaces.pdf | |
| PWC | https://paperswithcode.com/paper/multiscale-semi-markov-dynamics-for |
| Repo | |
| Framework | |
TextFlow: A Text Similarity Measure based on Continuous Sequences
| Title | TextFlow: A Text Similarity Measure based on Continuous Sequences |
| Authors | Yassine Mrabet, Halil Kilicoglu, Dina Demner-Fushman |
| Abstract | Text similarity measures are used in multiple tasks such as plagiarism detection, information ranking and recognition of paraphrases and textual entailment. While recent advances in deep learning highlighted the relevance of sequential models in natural language generation, existing similarity measures do not fully exploit the sequential nature of language. Examples of such similarity measures include n-grams and skip-grams overlap which rely on distinct slices of the input texts. In this paper we present a novel text similarity measure inspired from a common representation in DNA sequence alignment algorithms. The new measure, called TextFlow, represents input text pairs as continuous curves and uses both the actual position of the words and sequence matching to compute the similarity value. Our experiments on 8 different datasets show very encouraging results in paraphrase detection, textual entailment recognition and ranking relevance. |
| Tasks | Natural Language Inference, Text Generation |
| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/P17-1071/ |
| https://www.aclweb.org/anthology/P17-1071 | |
| PWC | https://paperswithcode.com/paper/textflow-a-text-similarity-measure-based-on |
| Repo | |
| Framework | |
A Computational Analysis of the Language of Drug Addiction
| Title | A Computational Analysis of the Language of Drug Addiction |
| Authors | Carlo Strapparava, Rada Mihalcea |
| Abstract | We present a computational analysis of the language of drug users when talking about their drug experiences. We introduce a new dataset of over 4,000 descriptions of experiences reported by users of four main drug types, and show that we can predict with an F1-score of up to 88{%} the drug behind a certain experience. We also perform an analysis of the dominant psycholinguistic processes and dominant emotions associated with each drug type, which sheds light on the characteristics of drug users. |
| Tasks | |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/E17-2022/ |
| https://www.aclweb.org/anthology/E17-2022 | |
| PWC | https://paperswithcode.com/paper/a-computational-analysis-of-the-language-of |
| Repo | |
| Framework | |
Deriving Word Prosody from Orthography in Hindi
| Title | Deriving Word Prosody from Orthography in Hindi |
| Authors | Somnath Roy |
| Abstract | |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7502/ |
| https://www.aclweb.org/anthology/W17-7502 | |
| PWC | https://paperswithcode.com/paper/deriving-word-prosody-from-orthography-in |
| Repo | |
| Framework | |
Proceedings of the Workshop on New Frontiers in Summarization
| Title | Proceedings of the Workshop on New Frontiers in Summarization |
| Authors | |
| Abstract | |
| Tasks | |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4500/ |
| https://www.aclweb.org/anthology/W17-4500 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-workshop-on-new-frontiers |
| Repo | |
| Framework | |
Obtaining referential word meanings from visual and distributional information: Experiments on object naming
| Title | Obtaining referential word meanings from visual and distributional information: Experiments on object naming |
| Authors | Sina Zarrie{\ss}, David Schlangen |
| Abstract | We investigate object naming, which is an important sub-task of referring expression generation on real-world images. As opposed to mutually exclusive labels used in object recognition, object names are more flexible, subject to communicative preferences and semantically related to each other. Therefore, we investigate models of referential word meaning that link visual to lexical information which we assume to be given through distributional word embeddings. We present a model that learns individual predictors for object names that link visual and distributional aspects of word meaning during training. We show that this is particularly beneficial for zero-shot learning, as compared to projecting visual objects directly into the distributional space. In a standard object naming task, we find that different ways of combining lexical and visual information achieve very similar performance, though experiments on model combination suggest that they capture complementary aspects of referential meaning. |
| Tasks | Object Classification, Object Recognition, Word Embeddings, Zero-Shot Learning |
| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/P17-1023/ |
| https://www.aclweb.org/anthology/P17-1023 | |
| PWC | https://paperswithcode.com/paper/obtaining-referential-word-meanings-from |
| Repo | |
| Framework | |
The AMU-UEdin Submission to the WMT 2017 Shared Task on Automatic Post-Editing
| Title | The AMU-UEdin Submission to the WMT 2017 Shared Task on Automatic Post-Editing |
| Authors | Marcin Junczys-Dowmunt, Marcin Junczys-Dowmunt |
| Abstract | |
| Tasks | Automatic Post-Editing, Machine Translation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4774/ |
| https://www.aclweb.org/anthology/W17-4774 | |
| PWC | https://paperswithcode.com/paper/the-amu-uedin-submission-to-the-wmt-2017 |
| Repo | |
| Framework | |
Continuous fluency tracking and the challenges of varying text complexity
| Title | Continuous fluency tracking and the challenges of varying text complexity |
| Authors | Beata Beigman Klebanov, Anastassia Loukina, John Sabatini, Tenaha O{'}Reilly |
| Abstract | This paper is a preliminary report on using text complexity measurement in the service of a new educational application. We describe a reading intervention where a child takes turns reading a book aloud with a virtual reading partner. Our ultimate goal is to provide meaningful feedback to the parent or the teacher by continuously tracking the child{'}s improvement in reading fluency. We show that this would not be a simple endeavor, due to an intricate relationship between text complexity from the point of view of comprehension and reading rate. |
| Tasks | Reading Comprehension |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-5003/ |
| https://www.aclweb.org/anthology/W17-5003 | |
| PWC | https://paperswithcode.com/paper/continuous-fluency-tracking-and-the |
| Repo | |
| Framework | |
Multi-objective radiomics model for predicting distant failure in lung SBRT
| Title | Multi-objective radiomics model for predicting distant failure in lung SBRT |
| Authors | Zhou Z1, Folkert M, Iyengar P, Westover K, Zhang Y, Choy H, Timmerman R, Jiang S, Wang J. |
| Abstract | Stereotactic body radiation therapy (SBRT) has demonstrated high local control rates in early stage non-small cell lung cancer patients who are not ideal surgical candidates. However, distant failure after SBRT is still common. For patients at high risk of early distant failure after SBRT treatment, additional systemic therapy may reduce the risk of distant relapse and improve overall survival. Therefore, a strategy that can correctly stratify patients at high risk of failure is needed. The field of radiomics holds great potential in predicting treatment outcomes by using high-throughput extraction of quantitative imaging features. The construction of predictive models in radiomics is typically based on a single objective such as overall accuracy or the area under the curve (AUC). However, because of imbalanced positive and negative events in the training datasets, a single objective may not be ideal to guide model construction. To overcome these limitations, we propose a multi-objective radiomics model that simultaneously considers sensitivity and specificity as objective functions. To design a more accurate and reliable model, an iterative multi-objective immune algorithm (IMIA) was proposed to optimize these objective functions. The multi-objective radiomics model is more sensitive than the single-objective model, while maintaining the same levels of specificity and AUC. The IMIA performs better than the traditional immune-inspired multi-objective algorithm. |
| Tasks | |
| Published | 2017-06-07 |
| URL | https://www.ncbi.nlm.nih.gov/pubmed/28480871 |
| https://iopscience.iop.org/article/10.1088/1361-6560/aa6ae5/pdf | |
| PWC | https://paperswithcode.com/paper/multi-objective-radiomics-model-for |
| Repo | |
| Framework | |
Finding Individual Word Sense Changes and their Delay in Appearance
| Title | Finding Individual Word Sense Changes and their Delay in Appearance |
| Authors | Nina Tahmasebi, Thomas Risse |
| Abstract | We present a method for detecting word sense changes by utilizing automatically induced word senses. Our method works on the level of individual senses and allows a word to have e.g. one stable sense and then add a novel sense that later experiences change. Senses are grouped based on polysemy to find linguistic concepts and we can find broadening and narrowing as well as novel (polysemous and homonymic) senses. We evaluate on a testset, present recall and estimates of the time between expected and found change. |
| Tasks | Word Embeddings, Word Sense Induction |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/R17-1095/ |
| https://doi.org/10.26615/978-954-452-049-6_095 | |
| PWC | https://paperswithcode.com/paper/finding-individual-word-sense-changes-and |
| Repo | |
| Framework | |
Learning bilingual word embeddings with (almost) no bilingual data
| Title | Learning bilingual word embeddings with (almost) no bilingual data |
| Authors | Mikel Artetxe, Gorka Labaka, Eneko Agirre |
| Abstract | Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs. This has motivated an active research line to relax this requirement, with methods that use document-aligned corpora or bilingual dictionaries of a few thousand words instead. In this work, we further reduce the need of bilingual resources using a very simple self-learning approach that can be combined with any dictionary-based mapping technique. Our method exploits the structural similarity of embedding spaces, and works with as little bilingual evidence as a 25 word dictionary or even an automatically generated list of numerals, obtaining results comparable to those of systems that use richer resources. |
| Tasks | Document Classification, Entity Linking, Machine Translation, Multilingual Word Embeddings, Part-Of-Speech Tagging, Transfer Learning, Word Embeddings |
| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/P17-1042/ |
| https://www.aclweb.org/anthology/P17-1042 | |
| PWC | https://paperswithcode.com/paper/learning-bilingual-word-embeddings-with |
| Repo | |
| Framework | |
A Corpus of Natural Language for Visual Reasoning
| Title | A Corpus of Natural Language for Visual Reasoning |
| Authors | Alane Suhr, Mike Lewis, James Yeh, Yoav Artzi |
| Abstract | We present a new visual reasoning language dataset, containing 92,244 pairs of examples of natural statements grounded in synthetic images with 3,962 unique sentences. We describe a method of crowdsourcing linguistically-diverse data, and present an analysis of our data. The data demonstrates a broad set of linguistic phenomena, requiring visual and set-theoretic reasoning. We experiment with various models, and show the data presents a strong challenge for future research. |
| Tasks | Question Answering, Visual Question Answering, Visual Reasoning |
| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/P17-2034/ |
| https://www.aclweb.org/anthology/P17-2034 | |
| PWC | https://paperswithcode.com/paper/a-corpus-of-natural-language-for-visual |
| Repo | |
| Framework | |
A Learning Error Analysis for Structured Prediction with Approximate Inference
| Title | A Learning Error Analysis for Structured Prediction with Approximate Inference |
| Authors | Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang, Xuanjing Huang |
| Abstract | In this work, we try to understand the differences between exact and approximate inference algorithms in structured prediction. We compare the estimation and approximation error of both underestimate and overestimate models. The result shows that, from the perspective of learning errors, performances of approximate inference could be as good as exact inference. The error analyses also suggest a new margin for existing learning algorithms. Empirical evaluations on text classification, sequential labelling and dependency parsing witness the success of approximate inference and the benefit of the proposed margin. |
| Tasks | Dependency Parsing, Structured Prediction, Text Classification |
| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/7193-a-learning-error-analysis-for-structured-prediction-with-approximate-inference |
| http://papers.nips.cc/paper/7193-a-learning-error-analysis-for-structured-prediction-with-approximate-inference.pdf | |
| PWC | https://paperswithcode.com/paper/a-learning-error-analysis-for-structured |
| Repo | |
| Framework | |
