Paper Group NANR 113
SemTagger: A Novel Approach for Semantic Similarity Based Hashtag Recommendation on Twitter. Tutorial for Deaf – Teaching Punjabi Alphabet using Synthetic Animations. Relationship Extraction based on Category of Medical Concepts from Lexical Contexts. Malayalam VerbFrames. ``A pessimist sees the difficulty in every opportunity; an optimist sees th …
SemTagger: A Novel Approach for Semantic Similarity Based Hashtag Recommendation on Twitter
| Title | SemTagger: A Novel Approach for Semantic Similarity Based Hashtag Recommendation on Twitter |
| Authors | Kuntal Dey, Ritvik Shrivastava, Saroj Kaushik, L Venkata Subramaniam |
| Abstract | |
| Tasks | Semantic Similarity, Semantic Textual Similarity |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7523/ |
| https://www.aclweb.org/anthology/W17-7523 | |
| PWC | https://paperswithcode.com/paper/semtagger-a-novel-approach-for-semantic |
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Tutorial for Deaf – Teaching Punjabi Alphabet using Synthetic Animations
| Title | Tutorial for Deaf – Teaching Punjabi Alphabet using Synthetic Animations |
| Authors | Lalit Goyal, Vishal Goyal |
| Abstract | |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7522/ |
| https://www.aclweb.org/anthology/W17-7522 | |
| PWC | https://paperswithcode.com/paper/tutorial-for-deaf-teaching-punjabi-alphabet |
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Relationship Extraction based on Category of Medical Concepts from Lexical Contexts
| Title | Relationship Extraction based on Category of Medical Concepts from Lexical Contexts |
| Authors | Anupam Mondal, Dipankar Das, B, Sivaji yopadhyay |
| Abstract | |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7527/ |
| https://www.aclweb.org/anthology/W17-7527 | |
| PWC | https://paperswithcode.com/paper/relationship-extraction-based-on-category-of |
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Malayalam VerbFrames
| Title | Malayalam VerbFrames |
| Authors | Jisha P Jayan, Asha S Nair, Govindaru V |
| Abstract | |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7530/ |
| https://www.aclweb.org/anthology/W17-7530 | |
| PWC | https://paperswithcode.com/paper/malayalam-verbframes |
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``A pessimist sees the difficulty in every opportunity; an optimist sees the opportunity in every difficulty’’ – Understanding the psycho-sociological influences to it
| Title | ``A pessimist sees the difficulty in every opportunity; an optimist sees the opportunity in every difficulty’’ – Understanding the psycho-sociological influences to it | |
| Authors | Updendra Kumar, Vishal Kumar Rana, Srinivas Pykl, Amitava Das |
| Abstract | |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7532/ |
| https://www.aclweb.org/anthology/W17-7532 | |
| PWC | https://paperswithcode.com/paper/a-pessimist-sees-the-difficulty-in-every |
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Lexical Correction of Polish Twitter Political Data
| Title | Lexical Correction of Polish Twitter Political Data |
| Authors | Maciej Ogrodniczuk, Mateusz Kope{'c} |
| Abstract | Language processing architectures are often evaluated in near-to-perfect conditions with respect to processed content. The tools which perform sufficiently well on electronic press, books and other type of non-interactive content may poorly handle littered, colloquial and multilingual textual data which make the majority of communication today. This paper aims at investigating how Polish Twitter data (in a slightly controlled {`}political{'} flavour) differs from expectation of linguistic tools and how they could be corrected to be ready for processing by standard language processing chains available for Polish. The setting includes specialised components for spelling correction of tweets as well as hashtag and username decoding. | |
| Tasks | Entity Extraction, Lemmatization, Spelling Correction |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2215/ |
| https://www.aclweb.org/anthology/W17-2215 | |
| PWC | https://paperswithcode.com/paper/lexical-correction-of-polish-twitter |
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deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter
| Title | deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter |
| Authors | Tzu-Hsuan Yang, Tzu-Hsuan Tseng, Chia-Ping Chen |
| Abstract | In this paper, we describe our system implementation for sentiment analysis in Twitter. This system combines two models based on deep neural networks, namely a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network, through interpolation. Distributed representation of words as vectors are input to the system, and the output is a sentiment class. The neural network models are trained exclusively with the data sets provided by the organizers of SemEval-2017 Task 4 Subtask A. Overall, this system has achieved 0.618 for the average recall rate, 0.587 for the average F1 score, and 0.618 for accuracy. |
| Tasks | Sentiment Analysis, Text Classification, Word Embeddings |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2101/ |
| https://www.aclweb.org/anthology/S17-2101 | |
| PWC | https://paperswithcode.com/paper/deepsa-at-semeval-2017-task-4-interpolated |
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Context Sensitive Lemmatization Using Two Successive Bidirectional Gated Recurrent Networks
| Title | Context Sensitive Lemmatization Using Two Successive Bidirectional Gated Recurrent Networks |
| Authors | Abhisek Chakrabarty, P, Onkar Arun it, Utpal Garain |
| Abstract | We introduce a composite deep neural network architecture for supervised and language independent context sensitive lemmatization. The proposed method considers the task as to identify the correct edit tree representing the transformation between a word-lemma pair. To find the lemma of a surface word, we exploit two successive bidirectional gated recurrent structures - the first one is used to extract the character level dependencies and the next one captures the contextual information of the given word. The key advantages of our model compared to the state-of-the-art lemmatizers such as Lemming and Morfette are - (i) it is independent of human decided features (ii) except the gold lemma, no other expensive morphological attribute is required for joint learning. We evaluate the lemmatizer on nine languages - Bengali, Catalan, Dutch, Hindi, Hungarian, Italian, Latin, Romanian and Spanish. It is found that except Bengali, the proposed method outperforms Lemming and Morfette on the other languages. To train the model on Bengali, we develop a gold lemma annotated dataset (having 1,702 sentences with a total of 20,257 word tokens), which is an additional contribution of this work. |
| Tasks | Lemmatization |
| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/P17-1136/ |
| https://www.aclweb.org/anthology/P17-1136 | |
| PWC | https://paperswithcode.com/paper/context-sensitive-lemmatization-using-two |
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Experiments with Domain Dependent Dialogue Act Classification using Open-Domain Dialogue Corpora
| Title | Experiments with Domain Dependent Dialogue Act Classification using Open-Domain Dialogue Corpora |
| Authors | Swapnil Hingmire, Apoorv Shrivastava, Girish Palshikar, Saurabh Srivastava |
| Abstract | |
| Tasks | Dialogue Act Classification |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7538/ |
| https://www.aclweb.org/anthology/W17-7538 | |
| PWC | https://paperswithcode.com/paper/experiments-with-domain-dependent-dialogue |
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Multilingwis\mbox$^2$ – Explore Your Parallel Corpus
| Title | Multilingwis\mbox$^2$ – Explore Your Parallel Corpus |
| Authors | Johannes Gra{"e}n, S, Dominique oz, Martin Volk |
| Abstract | |
| Tasks | Lemmatization, Word Alignment |
| Published | 2017-05-01 |
| URL | https://www.aclweb.org/anthology/W17-0231/ |
| https://www.aclweb.org/anthology/W17-0231 | |
| PWC | https://paperswithcode.com/paper/multilingwis2-axtendash-explore-your-parallel |
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Acronym Expansion: A General Approach Using Deep Learning
| Title | Acronym Expansion: A General Approach Using Deep Learning |
| Authors | Aditya Thakker, Suhail Barot, Sudhir Bagul |
| Abstract | |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7540/ |
| https://www.aclweb.org/anthology/W17-7540 | |
| PWC | https://paperswithcode.com/paper/acronym-expansion-a-general-approach-using |
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A Modified Cosine-Similarity based Log Kernel for Support Vector Machines in the Domain of Text Classification
| Title | A Modified Cosine-Similarity based Log Kernel for Support Vector Machines in the Domain of Text Classification |
| Authors | Rajendra Kumar Roul, Kushagr Arora |
| Abstract | |
| Tasks | Text Classification |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7542/ |
| https://www.aclweb.org/anthology/W17-7542 | |
| PWC | https://paperswithcode.com/paper/a-modified-cosine-similarity-based-log-kernel |
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A Deep Dive into Identification of Characters from Mahabharata
| Title | A Deep Dive into Identification of Characters from Mahabharata |
| Authors | Apurba Paul, Dipankar Das |
| Abstract | |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7555/ |
| https://www.aclweb.org/anthology/W17-7555 | |
| PWC | https://paperswithcode.com/paper/a-deep-dive-into-identification-of-characters |
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Rule Based Approch of Clause Boundary Identification in Telugu
| Title | Rule Based Approch of Clause Boundary Identification in Telugu |
| Authors | Ganthoti Nagaraju, Thennarasu Sakkan, Christopher Mala |
| Abstract | |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7550/ |
| https://www.aclweb.org/anthology/W17-7550 | |
| PWC | https://paperswithcode.com/paper/rule-based-approch-of-clause-boundary-identi |
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Spelling Correction for Morphologically Rich Language: a Case Study of Russian
| Title | Spelling Correction for Morphologically Rich Language: a Case Study of Russian |
| Authors | Alexey Sorokin |
| Abstract | We present an algorithm for automatic correction of spelling errors on the sentence level, which uses noisy channel model and feature-based reranking of hypotheses. Our system is designed for Russian and clearly outperforms the winner of SpellRuEval-2016 competition. We show that language model size has the greatest influence on spelling correction quality. We also experiment with different types of features and show that morphological and semantic information also improves the accuracy of spellchecking. |
| Tasks | Language Modelling, Lemmatization, Spelling Correction |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1408/ |
| https://www.aclweb.org/anthology/W17-1408 | |
| PWC | https://paperswithcode.com/paper/spelling-correction-for-morphologically-rich |
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