pwc-archive/papers-with-abstracts huggingface dataset:
SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering
Authors: Delphine Charlet, G{\'e}raldine Damnati
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Knowledge Graphs, Question Answering, Question Similarity, Relation, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

This paper describes the SimBow system submitted at SemEval2017-Task3, for the question-question similarity subtask B. The proposed approach is a supervised combination of different unsupervised textual similarities. These textual similarities rely on the introduction of a relation matrix in the classical cosine similarity between bag-of-words, so as to get a soft-cosine that takes into account relations between words. According to the type of relation matrix embedded in the soft-cosine, semantic or lexical relations can be considered. Our system ranked first among the official submissions of subtask B.
FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering
Authors: Sheng Zhang, Jiajun Cheng, Hui Wang, Xin Zhang, Pei Li, Zhaoyun Ding
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Answer Selection, Community Question Answering, Question Answering

We describes deep neural networks frameworks in this paper to address the community question answering (cQA) ranking task (SemEval-2017 task 3). Convolutional neural networks and bi-directional long-short term memory networks are applied in our methods to extract semantic information from questions and answers (comments). In addition, in order to take the full advantage of question-comment semantic relevance, we deploy interaction layer and augmented features before calculating the similarity. The results show that our methods have the great effectiveness for both subtask A and subtask C.
KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering
Authors: Simone Filice, Giovanni Da San Martino, Aless Moschitti, ro
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Question Answering, Relational Reasoning, Sentence, Sentence Pair Modeling

This paper describes the KeLP system participating in the SemEval-2017 community Question Answering (cQA) task. The system is a refinement of the kernel-based sentence pair modeling we proposed for the previous year challenge. It is implemented within the Kernel-based Learning Platform called KeLP, from which we inherit the team{'}s name. Our primary submission ranked first in subtask A, and third in subtasks B and C, being the only systems appearing in the top-3 ranking for all the English subtasks. This shows that the proposed framework, which has minor variations among the three subtasks, is extremely flexible and effective in tackling learning tasks defined on sentence pairs.
SwissAlps at SemEval-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering
Authors: Jan Milan Deriu, Mark Cieliebak
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Question Answering, Question Similarity, Reranking

In this paper we propose a system for reranking answers for a given question. Our method builds on a siamese CNN architecture which is extended by two attention mechanisms. The approach was evaluated on the datasets of the SemEval-2017 competition for Community Question Answering (cQA), where it achieved 7th place obtaining a MAP score of 86:24 points on the Question-Comment Similarity subtask.
TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA
Authors: Filip {\v{S}}aina, Toni Kukurin, Lukrecija Pulji{\'c}, Mladen Karan, Jan {\v{S}}najder
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, General Classification, Information Retrieval, Question Answering, Re-Ranking, Retrieval, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

In this paper we present the TakeLab-QA entry to SemEval 2017 task 3, which is a question-comment re-ranking problem. We present a classification based approach, including two supervised learning models {--} Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). We use features based on different semantic similarity models (e.g., Latent Dirichlet Allocation), as well as features based on several types of pre-trained word embeddings. Moreover, we also use some hand-crafted task-specific features. For training, our system uses no external labeled data apart from that provided by the organizers. Our primary submission achieves a MAP-score of 81.14 and F1-score of 66.99 {--} ranking us 10th on the SemEval 2017 task 3, subtask A.
GW\_QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic Fora
Authors: Nada Almarwani, Mona Diab
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Answer Selection, BIG-bench Machine Learning, Community Question Answering, Question Answering, Re-Ranking

This paper describes our submission to SemEval-2017 Task 3 Subtask D, {``}Question Answer Ranking in Arabic Community Question Answering{''}. In this work, we applied a supervised machine learning approach to automatically re-rank a set of QA pairs according to their relevance to a given question. We employ features based on latent semantic models, namely WTMF, as well as a set of lexical features based on string lengths and surface level matching. The proposed system ranked first out of 3 submissions, with a MAP score of 61.16{\%}.
NLM\_NIH at SemEval-2017 Task 3: from Question Entailment to Question Similarity for Community Question Answering
Authors: Asma Ben Abacha, Dina Demner-Fushman
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Question Answering, Question Similarity

This paper describes our participation in SemEval-2017 Task 3 on Community Question Answering (cQA). The Question Similarity subtask (B) aims to rank a set of related questions retrieved by a search engine according to their similarity to the original question. We adapted our feature-based system for Recognizing Question Entailment (RQE) to the question similarity task. Tested on cQA-B-2016 test data, our RQE system outperformed the best system of the 2016 challenge in all measures with 77.47 MAP and 80.57 Accuracy. On cQA-B-2017 test data, performances of all systems dropped by around 30 points. Our primary system obtained 44.62 MAP, 67.27 Accuracy and 47.25 F1 score. The cQA-B-2017 best system achieved 47.22 MAP and 42.37 F1 score. Our system is ranked sixth in terms of MAP and third in terms of F1 out of 13 participating teams.
bunji at SemEval-2017 Task 3: Combination of Neural Similarity Features and Comment Plausibility Features
Authors: Yuta Koreeda, Takuya Hashito, Yoshiki Niwa, Misa Sato, Toshihiko Yanase, Kenzo Kurotsuchi, Kohsuke Yanai
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Question Answering, Question Similarity

This paper describes a text-ranking system developed by bunji team in SemEval-2017 Task 3: Community Question Answering, Subtask A and C. The goal of the task is to re-rank the comments in a question-and-answer forum such that useful comments for answering the question are ranked high. We proposed a method that combines neural similarity features and hand-crafted comment plausibility features, and we modeled inter-comments relationship using conditional random field. Our approach obtained the fifth place in the Subtask A and the second place in the Subtask C.
QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering Forums
Authors: Marwan Torki, Maram Hasanain, Tamer Elsayed
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Question Answering, Semantic Textual Similarity

In this paper we describe our QU-BIGIR system for the Arabic subtask D of the SemEval 2017 Task 3. Our approach builds on our participation in the past version of the same subtask. This year, our system uses different similarity measures that encodes lexical and semantic pairwise similarity of text pairs. In addition to well known similarity measures such as cosine similarity, we use other measures based on the summary statistics of word embedding representation for a given text. To rank a list of candidate question answer pairs for a given question, we learn a linear SVM classifier over our similarity features. Our best resulting run came second in subtask D with a very competitive performance to the first-ranking system.
ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task
Authors: Guoshun Wu, Yixuan Sheng, Man Lan, Yuanbin Wu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Question Answering, Question Similarity, Semantic Textual Similarity

This paper describes the systems we submitted to the task 3 (Community Question Answering) in SemEval 2017 which contains three subtasks on English corpora, i.e., subtask A: Question-Comment Similarity, subtask B: Question-Question Similarity, and subtask C: Question-External Comment Similarity. For subtask A, we combined two different methods to represent question-comment pair, i.e., supervised model using traditional features and Convolutional Neural Network. For subtask B, we utilized the information of snippets returned from Search Engine with question subject as query. For subtask C, we ranked the comments by multiplying the probability of the pair related question comment being Good by the reciprocal rank of the related question.
UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA
Authors: Surya Agustian, Hiroya Takamura
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Knowledge Graphs, Position, Question Answering, Semantic Textual Similarity, Word Embeddings

The majority of core techniques to solve many problems in Community Question Answering (CQA) task rely on similarity computation. This work focuses on similarity between two sentences (or questions in subtask B) based on word embeddings. We exploit words importance levels in sentences or questions for similarity features, for classification and ranking with machine learning. Using only 2 types of similarity metric, our proposed method has shown comparable results with other complex systems. This method on subtask B 2017 dataset is ranked on position 7 out of 13 participants. Evaluation on 2016 dataset is on position 8 of 12, outperforms some complex systems. Further, this finding is explorable and potential to be used as baseline and extensible for many tasks in CQA and other textual similarity based system.
Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection
Authors: Byron Galbraith, Bhanu Pratap, Daniel Shank
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, General Classification, Information Retrieval, Question Answering, Question Similarity, Semantic Similarity, Semantic Textual Similarity

This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions are paraphrases of each other. This approach achieved a MAP of 45.7{\%} out of max achievable 67.0{\%} on the test set.
QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter
Authors: Xiwu Han, Gregory Toner
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Humor Detection, imbalanced classification

This paper presents our submission to SemEval-2017 Task 6: {\#}HashtagWars: Learning a Sense of Humor. There are two subtasks: A. Pairwise Comparison, and B. Semi-Ranking. Our assumption is that the distribution of humorous and non-humorous texts in real life language is naturally imbalanced. Using Na{\"\i}ve Bayes Multinomial with standard text-representation features, we approached Subtask B as a sequence of imbalanced classification problems, and optimized our system per the macro-average recall. Subtask A was then solved via the Semi-Ranking results. On the final test, our system was ranked 10th for Subtask A, and 3rd for Subtask B.
DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison
Authors: Christos Baziotis, Nikos Pelekis, Christos Doulkeridis
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Feature Engineering, Humor Detection, Word Embeddings

In this paper we present a deep-learning system that competed at SemEval-2017 Task 6 ''{\#}HashtagWars: Learning a Sense of Humor{''}. We participated in Subtask A, in which the goal was, given two Twitter messages, to identify which one is funnier. We propose a Siamese architecture with bidirectional Long Short-Term Memory (LSTM) networks, augmented with an attention mechanism. Our system works on the token-level, leveraging word embeddings trained on a big collection of unlabeled Twitter messages. We ranked 2nd in 7 teams. A post-completion improvement of our model, achieves state-of-the-art results on {\#}HashtagWars dataset.
TakeLab at SemEval-2017 Task 6: \#RankingHumorIn4Pages
Authors: Marin Kukova{\v{c}}ec, Juraj Malenica, Ivan Mr{\v{s}}i{\'c}, Antonio {\v{S}}ajatovi{\'c}, Domagoj Alagi{\'c}, Jan {\v{S}}najder
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Common Sense Reasoning, Humor Detection

This paper describes our system for humor ranking in tweets within the SemEval 2017 Task 6: {\#}HashtagWars (6A and 6B). For both subtasks, we use an off-the-shelf gradient boosting model built on a rich set of features, handcrafted to provide the model with the external knowledge needed to better predict the humor in the text. The features capture various cultural references and specific humor patterns. Our system ranked 2nd (officially 7th) among 10 submissions on the Subtask A and 2nd among 9 submissions on the Subtask B.
SRHR at SemEval-2017 Task 6: Word Associations for Humour Recognition
Authors: Andrew Cattle, Xiaojuan Ma
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Avg

This paper explores the role of semantic relatedness features, such as word associations, in humour recognition. Specifically, we examine the task of inferring pairwise humour judgments in Twitter hashtag wars. We examine a variety of word association features derived from University of Southern Florida Free Association Norms (USF) and the Edinburgh Associative Thesaurus (EAT) and find that word association-based features outperform Word2Vec similarity, a popular semantic relatedness measure. Our system achieves an accuracy of 56.42{\%} using a combination of unigram perplexity, bigram perplexity, EAT difference (tweet-avg), USF forward (max), EAT difference (word-avg), USF difference (word-avg), EAT forward (min), USF difference (tweet-max), and EAT backward (min).
\#WarTeam at SemEval-2017 Task 6: Using Neural Networks for Discovering Humorous Tweets
Authors: Iuliana Alex Fle{\textcommabelow{s}}can-Lovin-Arseni, ra, Ramona Andreea Turcu, Cristina S{\^\i}rbu, Larisa Alexa, Amar, S ei, ra Maria, Nichita Herciu, Constantin Scutaru, Tr, Diana ab{\u{a}}{\textcommabelow{t}}, Adrian Iftene
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Humor Detection

This paper presents the participation of {\#}WarTeam in Task 6 of SemEval2017 with a system classifying humor by comparing and ranking tweets. The training data consists of annotated tweets from the @midnight TV show. {\#}WarTeam{'}s system uses a neural network (TensorFlow) having inputs from a Na{\"\i}ve Bayes humor classifier and a sentiment analyzer.
SVNIT @ SemEval 2017 Task-6: Learning a Sense of Humor Using Supervised Approach
Authors: Rutal Mahajan, Mukesh Zaveri
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Opinion Mining, Sentiment Analysis

This paper describes the system devel-oped for SemEval 2017 task 6: {\#}HashTagWars -Learning a Sense of Hu-mor. Learning to recognize sense of hu-mor is the important task for language understanding applications. Different set of features based on frequency of words, structure of tweets and semantics are used in this system to identify the presence of humor in tweets. Supervised machine learning approaches, Multilayer percep-tron and Na{\"\i}ve Bayes are used to classify the tweets in to three level of sense of humor. For given Hashtag, the system finds the funniest tweet and predicts the amount of funniness of all the other tweets. In official submitted runs, we have achieved 0.506 accuracy using mul-tilayer perceptron in subtask-A and 0.938 distance in subtask-B. Using Na{\"\i}ve bayes in subtask-B, the system achieved 0.949 distance. Apart from official runs, this system have scored 0.751 accuracy in subtask-A using SVM. But still there is a wide room for improvement in system.
UWaterloo at SemEval-2017 Task 7: Locating the Pun Using Syntactic Characteristics and Corpus-based Metrics
Authors: Olga Vechtomova
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Position

The paper presents a system for locating a pun word. The developed method calculates a score for each word in a pun, using a number of components, including its Inverse Document Frequency (IDF), Normalized Pointwise Mutual Information (NPMI) with other words in the pun text, its position in the text, part-of-speech and some syntactic features. The method achieved the best performance in the Heterographic category and the second best in the Homographic. Further analysis showed that IDF is the most useful characteristic, whereas the count of words with which the given word has high NPMI has a negative effect on performance.
JU CSE NLP @ SemEval 2017 Task 7: Employing Rules to Detect and Interpret English Puns
Authors: Aniket Pramanick, Dipankar Das
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Word Sense Disambiguation

System description. Implementation of HMM and Cyclic Dependency Network.
N-Hance at SemEval-2017 Task 7: A Computational Approach using Word Association for Puns
Authors: {\"O}zge Sevgili, Nima Ghotbi, Selma Tekir
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentence, Word Sense Disambiguation

This paper presents a system developed for SemEval-2017 Task 7, Detection and Interpretation of English Puns consisting of three subtasks; pun detection, pun location, and pun interpretation, respectively. The system stands on recognizing a distinctive word which has a high association with the pun in the given sentence. The intended humorous meaning of pun is identified through the use of this word. Our official results confirm the potential of this approach.
ELiRF-UPV at SemEval-2017 Task 7: Pun Detection and Interpretation
Authors: Llu{\'\i}s-F. Hurtado, Encarna Segarra, Ferran Pla, Pascual Carrasco, Jos{\'e}-{\'A}ngel Gonz{\'a}lez
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentence, Word Embeddings, Word Sense Disambiguation

This paper describes the participation of ELiRF-UPV team at task 7 (subtask 2: homographic pun detection and subtask 3: homographic pun interpretation) of SemEval2017. Our approach is based on the use of word embeddings to find related words in a sentence and a version of the Lesk algorithm to establish relationships between synsets. The results obtained are in line with those obtained by the other participants and they encourage us to continue working on this problem.
BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic English Puns with Sense Embeddings
Authors: Dieke Oele, Kilian Evang
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Word Sense Disambiguation

This paper describes our system participating in the SemEval-2017 Task 7, for the subtasks of homographic pun location and homographic pun interpretation. For pun interpretation, we use a knowledge-based Word Sense Disambiguation (WSD) method based on sense embeddings. Pun-based jokes can be divided into two parts, each containing information about the two distinct senses of the pun. To exploit this structure we split the context that is input to the WSD system into two local contexts and find the best sense for each of them. We use the output of pun interpretation for pun location. As we expect the two meanings of a pun to be very dissimilar, we compute sense embedding cosine distances for each sense-pair and select the word that has the highest distance. We describe experiments on different methods of splitting the context and compare our method to several baselines. We find evidence supporting our hypotheses and obtain competitive results for pun interpretation.
UWAV at SemEval-2017 Task 7: Automated feature-based system for locating puns
Authors: Ankit Vadehra
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: feature selection, Language Modeling, Language Modelling, Machine Translation, Sentiment Analysis, Word Sense Disambiguation

In this paper we describe our system created for SemEval-2017 Task 7: Detection and Interpretation of English Puns. We tackle subtask 1, pun detection, by leveraging features selected from sentences to design a classifier that can disambiguate between the presence or absence of a pun. We address subtask 2, pun location, by utilizing a decision flow structure that uses presence or absence of certain features to decide the next action. The results obtained by our system are encouraging, considering the simplicity of the system. We consider this system as a precursor for deeper exploration on efficient feature selection for pun detection.
ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English Puns
Authors: Yuhuan Xiu, Man Lan, Yuanbin Wu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Part-Of-Speech Tagging, Semantic Textual Similarity, Sentence, Word Sense Disambiguation

This paper describes our submissions to task 7 in SemEval 2017, i.e., Detection and Interpretation of English Puns. We participated in the first two subtasks, which are to detect and locate English puns respectively. For subtask 1, we presented a supervised system to determine whether or not a sentence contains a pun using similarity features calculated on sense vectors or cluster center vectors. For subtask 2, we established an unsupervised system to locate the pun by scoring each word in the sentence and we assumed that the word with the smallest score is the pun.
Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language
Authors: Vijayasaradhi Indurthi, Subba Reddy Oota
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Feature Engineering, Word Embeddings

This paper describes our system for detection and interpretation of English puns. We participated in 2 subtasks related to homographic puns achieve comparable results for these tasks. Through the paper we provide detailed description of the approach, as well as the results obtained in the task. Our models achieved a F1-score of 77.65{\%} for Subtask 1 and 52.15{\%} for Subtask 2.