Paper Group NANR 57
The Component Unit. Introducing a Novel Unit of Syntactic Analysis. ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification. RACAI’s Natural Language Processing pipeline for Universal Dependencies. Classification of modal meaning in negotiation dialogues. Sogou Neural Machin …
The Component Unit. Introducing a Novel Unit of Syntactic Analysis
| Title | The Component Unit. Introducing a Novel Unit of Syntactic Analysis |
| Authors | Timothy Osborne, Ruochen Niu |
| Abstract | |
| Tasks | Chunking |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-6520/ |
| https://www.aclweb.org/anthology/W17-6520 | |
| PWC | https://paperswithcode.com/paper/the-component-unit-introducing-a-novel-unit |
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ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification
| Title | ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification |
| Authors | Yunxiao Zhou, Man Lan, Yuanbin Wu |
| Abstract | This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that our system ranked above average. |
| Tasks | Feature Engineering, Lemmatization, Sentiment Analysis, Tokenization, Word Embeddings |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2137/ |
| https://www.aclweb.org/anthology/S17-2137 | |
| PWC | https://paperswithcode.com/paper/ecnu-at-semeval-2017-task-4-evaluating |
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RACAI’s Natural Language Processing pipeline for Universal Dependencies
| Title | RACAI’s Natural Language Processing pipeline for Universal Dependencies |
| Authors | Stefan Daniel Dumitrescu, Tiberiu Boros, Dan Tufis |
| Abstract | This paper presents RACAI{'}s approach, experiments and results at CONLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. We handle raw text and we cover tokenization, sentence splitting, word segmentation, tagging, lemmatization and parsing. All results are reported under strict training, development and testing conditions, in which the corpora provided for the shared tasks is used {``}as is{''}, without any modifications to the composition of the train and development sets. | |
| Tasks | Lemmatization, Tokenization |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/K17-3018/ |
| https://www.aclweb.org/anthology/K17-3018 | |
| PWC | https://paperswithcode.com/paper/racais-natural-language-processing-pipeline |
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Classification of modal meaning in negotiation dialogues
| Title | Classification of modal meaning in negotiation dialogues |
| Authors | Valeria Lapina, Volha Petukhova |
| Abstract | |
| Tasks | |
| Published | 2017-01-01 |
| URL | https://www.aclweb.org/anthology/W17-7406/ |
| https://www.aclweb.org/anthology/W17-7406 | |
| PWC | https://paperswithcode.com/paper/classification-of-modal-meaning-in |
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Sogou Neural Machine Translation Systems for WMT17
| Title | Sogou Neural Machine Translation Systems for WMT17 |
| Authors | Yuguang Wang, Shanbo Cheng, Liyang Jiang, Jiajun Yang, Wei Chen, Muze Li, Lin Shi, Yanfeng Wang, Hongtao Yang |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4742/ |
| https://www.aclweb.org/anthology/W17-4742 | |
| PWC | https://paperswithcode.com/paper/sogou-neural-machine-translation-systems-for |
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Proceedings of the Workshop on Stylistic Variation
| Title | Proceedings of the Workshop on Stylistic Variation |
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| Abstract | |
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| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4900/ |
| https://www.aclweb.org/anthology/W17-4900 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-workshop-on-stylistic |
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Latent Space Embedding for Retrieval in Question-Answer Archives
| Title | Latent Space Embedding for Retrieval in Question-Answer Archives |
| Authors | Deepak P, Dinesh Garg, Shirish Shevade |
| Abstract | Community-driven Question Answering (CQA) systems such as Yahoo! Answers have become valuable sources of reusable information. CQA retrieval enables usage of historical CQA archives to solve new questions posed by users. This task has received much recent attention, with methods building upon literature from translation models, topic models, and deep learning. In this paper, we devise a CQA retrieval technique, LASER-QA, that embeds question-answer pairs within a unified latent space preserving the local neighborhood structure of question and answer spaces. The idea is that such a space mirrors semantic similarity among questions as well as answers, thereby enabling high quality retrieval. Through an empirical analysis on various real-world QA datasets, we illustrate the improved effectiveness of LASER-QA over state-of-the-art methods. |
| Tasks | Question Answering, Semantic Similarity, Semantic Textual Similarity, Topic Models |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/D17-1089/ |
| https://www.aclweb.org/anthology/D17-1089 | |
| PWC | https://paperswithcode.com/paper/latent-space-embedding-for-retrieval-in |
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CUNI System for the WMT17 Multimodal Translation Task
| Title | CUNI System for the WMT17 Multimodal Translation Task |
| Authors | Jind{\v{r}}ich Helcl, Jind{\v{r}}ich Libovick{'y} |
| Abstract | |
| Tasks | Image Captioning, Machine Translation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4749/ |
| https://www.aclweb.org/anthology/W17-4749 | |
| PWC | https://paperswithcode.com/paper/cuni-system-for-the-wmt17-multimodal |
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Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach
| Title | Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach |
| Authors | Kishaloy Halder, Lahari Poddar, Min-Yen Kan |
| Abstract | Patients turn to Online Health Communities not only for information on specific conditions but also for emotional support. Previous research has indicated that the progression of emotional status can be studied through the linguistic patterns of an individual{'}s posts. We analyze a real-world dataset from the Mental Health section of HealthBoards.com. Estimated from the word usages in their posts, we find that the emotional progress across patients vary widely. We study the problem of predicting a patient{'}s emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it. We find that the future emotional status can be predicted with reasonable accuracy given her historical posts and participation features. Our evaluation results demonstrate the efficacy of our proposed architecture, by outperforming state-of-the-art approaches with over 0.13 reduction in Mean Absolute Error. |
| Tasks | |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-5217/ |
| https://www.aclweb.org/anthology/W17-5217 | |
| PWC | https://paperswithcode.com/paper/modeling-temporal-progression-of-emotional |
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Sharpness, Restart and Acceleration
| Title | Sharpness, Restart and Acceleration |
| Authors | Vincent Roulet, Alexandre D’Aspremont |
| Abstract | The {\L}ojasiewicz inequality shows that H"olderian error bounds on the minimum of convex optimization problems hold almost generically. Here, we clarify results of \citet{Nemi85} who show that H"olderian error bounds directly controls the performance of restart schemes. The constants quantifying error bounds are of course unobservable, but we show that optimal restart strategies are robust, and searching for the best scheme only increases the complexity by a logarithmic factor compared to the optimal bound. Overall then, restart schemes generically accelerate accelerated methods. |
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| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/6712-sharpness-restart-and-acceleration |
| http://papers.nips.cc/paper/6712-sharpness-restart-and-acceleration.pdf | |
| PWC | https://paperswithcode.com/paper/sharpness-restart-and-acceleration |
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YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model
| Title | YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model |
| Authors | Yuanye He, Liang-Chih Yu, K. Robert Lai, Weiyi Liu |
| Abstract | The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is expressed in a tweet. Compared to classification tasks that identify 1 among n emotions for a tweet, the present task can provide more fine-grained (real-valued) sentiment analysis. This paper presents a system that uses a bi-directional LSTM-CNN model to complete the competition task. Combining bi-directional LSTM and CNN, the prediction process considers both global information in a tweet and local important information. The proposed method ranked sixth among twenty-one teams in terms of Pearson Correlation Coefficient. |
| Tasks | Sentiment Analysis, Word Embeddings |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-5233/ |
| https://www.aclweb.org/anthology/W17-5233 | |
| PWC | https://paperswithcode.com/paper/yzu-nlp-at-emoint-2017-determining-emotion |
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基於半監督式學習之廣播節目語音逐字稿自動轉寫系統 (Automatic Transcription of Broadcast Radio Speech Based on Quality Estimation-Guided Semi-Supervised Training) [In Chinese]
| Title | 基於半監督式學習之廣播節目語音逐字稿自動轉寫系統 (Automatic Transcription of Broadcast Radio Speech Based on Quality Estimation-Guided Semi-Supervised Training) [In Chinese] |
| Authors | Sing-Yue Wang, Wu-Hua Hsu, Yuan-Fu Liao |
| Abstract | |
| Tasks | |
| Published | 2017-11-01 |
| URL | https://www.aclweb.org/anthology/O17-1020/ |
| https://www.aclweb.org/anthology/O17-1020 | |
| PWC | https://paperswithcode.com/paper/ao14acca14a-ca1ac-ceae3eac-eaae12a-c3c |
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Proceedings of the 11th Linguistic Annotation Workshop
| Title | Proceedings of the 11th Linguistic Annotation Workshop |
| Authors | |
| Abstract | |
| Tasks | |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-0800/ |
| https://www.aclweb.org/anthology/W17-0800 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-11th-linguistic-annotation |
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Feature-Enriched Character-Level Convolutions for Text Regression
| Title | Feature-Enriched Character-Level Convolutions for Text Regression |
| Authors | Gustavo Paetzold, Lucia Specia |
| Abstract | |
| Tasks | Feature Engineering, Machine Translation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4765/ |
| https://www.aclweb.org/anthology/W17-4765 | |
| PWC | https://paperswithcode.com/paper/feature-enriched-character-level-convolutions |
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Morphological Analysis of the Dravidian Language Family
| Title | Morphological Analysis of the Dravidian Language Family |
| Authors | Arun Kumar, Ryan Cotterell, Llu{'\i}s Padr{'o}, Antoni Oliver |
| Abstract | The Dravidian languages are one of the most widely spoken language families in the world, yet there are very few annotated resources available to NLP researchers. To remedy this, we create DravMorph, a corpus annotated for morphological segmentation and part-of-speech. Additionally, we exploit novel features and higher-order models to set state-of-the-art results on these corpora on both tasks, beating techniques proposed in the literature by as much as 4 points in segmentation F1. |
| Tasks | Morphological Analysis |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/E17-2035/ |
| https://www.aclweb.org/anthology/E17-2035 | |
| PWC | https://paperswithcode.com/paper/morphological-analysis-of-the-dravidian |
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