pwc-archive/papers-with-abstracts huggingface dataset:
INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines
Authors: Tiago Zini, Karin Becker, Marcelo Dias
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Opinion Mining, Sentiment Analysis

This paper describes a supervised solution for detecting the polarity scores of tweets or headline news in the financial domain, submitted to the SemEval 2017 Fine-Grained Sentiment Analysis on Financial Microblogs and News Task. The premise is that it is possible to understand market reaction over a company stock by measuring the positive/negative sentiment contained in the financial tweets and news headlines, where polarity is measured in a continuous scale ranging from -1.0 (very bearish) to 1.0 (very bullish). Our system receives as input the textual content of tweets or news headlines, together with their ids, stock cashtag or name of target company, and the polarity score gold standard for the training dataset. Our solution retrieves features from these text instances using n-gram, hashtags, sentiment score calculated by a external APIs and others features to train a regression model capable to detect continuous score of these sentiments with precision.
HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks
Authors: Lidia Pivovarova, Lloren{\c{c}} Escoter, Arto Klami, Roman Yangarber
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Data Augmentation, Sentiment Analysis

Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial microblogs and news. Our solution for determining the sentiment score extends an earlier convolutional neural network for sentiment analysis in several ways. We explicitly encode a focus on a particular company, we apply a data augmentation scheme, and use a larger data collection to complement the small training data provided by the task organizers. The best results were achieved by training a model on an external dataset and then tuning it using the provided training dataset.
NLG301 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Authors: Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentiment Analysis, TAR

Short length, multi-targets, target relation-ship, monetary expressions, and outside reference are characteristics of financial tweets. This paper proposes methods to extract target spans from a tweet and its referencing web page. Total 15 publicly available sentiment dictionaries and one sentiment dictionary constructed from training set, containing sentiment scores in binary or real numbers, are used to compute the sentiment scores of text spans. Moreover, the correlation coeffi-cients of the price return between any two stocks are learned with the price data from Bloomberg. They are used to capture the relationships between the interesting tar-get and other stocks mentioned in a tweet. The best result of our method in both sub-task are 56.68{\%} and 55.43{\%}, evaluated by evaluation method 2.
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter
Authors: Quanzhi Li, Sameena Shah, Armineh Nourbakhsh, Rui Fang, Xiaomo Liu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentiment Analysis, Word Embeddings

This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs. We use three types of word embeddings in our algorithm: word embeddings learned from 200 million tweets, sentiment-specific word embeddings learned from 10 million tweets using distance supervision, and word embeddings learned from 20 million StockTwits messages. In our approach, we also take the left and right context of the target company into consideration when generating polarity prediction features. All the features generated from different word embeddings and contexts are integrated together to train our algorithm
SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets
Authors: Narges Tabari, Armin Seyeditabari, Wlodek Zadrozny
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Binary Classification, Sentiment Analysis, Text Categorization

Sentiment analysis is the process of identifying the opinion expressed in text. Recently it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. SemEval-2017 task 5 focuses on the financial market as the domain for sentiment analysis of text; specifically, task 5, subtask 1 focuses on financial tweets about stock symbols. In this paper, we describe a machine learning classifier for binary classification of financial tweets. We used natural language processing techniques and the random forest algorithm to train our model, and tuned it for the training dataset of Task 5, subtask 1. Our system achieves the 7th rank on the leaderboard of the task.
DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles
Authors: Symeon Symeonidis, John Kordonis, Dimitrios Effrosynidis, Avi Arampatzis
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Articles, BIG-bench Machine Learning, Feature Engineering, Information Retrieval, regression, Sentiment Analysis

We present the system developed by the team DUTH for the participation in Semeval-2017 task 5 - Fine-Grained Sentiment Analysis on Financial Microblogs and News, in subtasks A and B. Our approach to determine the sentiment of Microblog Messages and News Statements {\&} Headlines is based on linguistic preprocessing, feature engineering, and supervised machine learning techniques. To train our model, we used Neural Network Regression, Linear Regression, Boosted Decision Tree Regression and Decision Forrest Regression classifiers to forecast sentiment scores. At the end, we present an error measure, so as to improve the performance about forecasting methods of the system.
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
Authors: Leon Rotim, Martin Tutek, Jan {\v{S}}najder
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Feature Engineering, regression, Sentiment Analysis, Word Embeddings

This paper describes our system for fine-grained sentiment scoring of news headlines submitted to SemEval 2017 task 5{--}subtask 2. Our system uses a feature-light method that consists of a Support Vector Regression (SVR) with various kernels and word vectors as features. Our best-performing submission scored 3rd on the task out of 29 teams and 4th out of 45 submissions with a cosine score of 0.733.
UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation
Authors: Vineet John, Olga Vechtomova
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Data Augmentation, regression, Sentiment Analysis

This paper discusses the approach taken by the UWaterloo team to arrive at a solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of SemEval 2017. The paper describes the document vectorization and sentiment score prediction techniques used, as well as the design and implementation decisions taken while building the system for this task. The system uses text vectorization models, such as N-gram, TF-IDF and paragraph embeddings, coupled with regression model variants to predict the sentiment scores. Amongst the methods examined, unigrams and bigrams coupled with simple linear regression obtained the best baseline accuracy. The paper also explores data augmentation methods to supplement the training dataset. This system was designed for Subtask 2 (News Statements and Headlines).
RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks
Authors: Sudipta Kar, Suraj Maharjan, Thamar Solorio
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentiment Analysis

In this paper, we present our systems for the {``}SemEval-2017 Task-5 on Fine-Grained Sentiment Analysis on Financial Microblogs and News{''}. In our system, we combined hand-engineered lexical, sentiment and metadata features, the representations learned from Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU) with Attention model applied on top. With this architecture we obtained weighted cosine similarity scores of 0.72 and 0.74 for subtask-1 and subtask-2, respectively. Using the official scoring system, our system ranked the second place for subtask-2 and eighth place for the subtask-1. It ranked first for both of the subtasks by the scores achieved by an alternate scoring system.
COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines
Authors: Kim Schouten, Flavius Frasincar, Franciska de Jong
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Position, Sentiment Analysis

This paper describes our submission to Task 5 of SemEval 2017, Fine-Grained Sentiment Analysis on Financial Microblogs and News, where we limit ourselves to performing sentiment analysis on news headlines only (track 2). The approach presented in this paper uses a Support Vector Machine to do the required regression, and besides unigrams and a sentiment tool, we use various ontology-based features. To this end we created a domain ontology that models various concepts from the financial domain. This allows us to model the sentiment of actions depending on which entity they are affecting (e.g., {`}decreasing debt{'} is positive, but {`}decreasing profit{'} is negative). The presented approach yielded a cosine distance of 0.6810 on the official test data, resulting in the 12th position.
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
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Feature Engineering, regression, Sentiment Analysis

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.
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text
Authors: Abhishek Kumar, Abhishek Sethi, Md. Shad Akhtar, Asif Ekbal, Chris Biemann, Pushpak Bhattacharyya
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: regression, Sentiment Analysis, Word Embeddings

This paper reports team IITPB{'}s participation in the SemEval 2017 Task 5 on {`}Fine-grained sentiment analysis on financial microblogs and news{'}. We developed 2 systems for the two tracks. One system was based on an ensemble of Support Vector Classifier and Logistic Regression. This system relied on Distributional Thesaurus (DT), word embeddings and lexicon features to predict a floating sentiment value between -1 and +1. The other system was based on Support Vector Regression using word embeddings, lexicon features, and PMI scores as features. The system was ranked 5th in track 1 and 8th in track 2.
IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis
Authors: Deepanway Ghosal, Shobhit Bhatnagar, Md. Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentiment Analysis

In this paper we propose an ensemble based model which combines state of the art deep learning sentiment analysis algorithms like Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) along with feature based models to identify optimistic or pessimistic sentiments associated with companies and stocks in financial texts. We build our system to participate in a competition organized by Semantic Evaluation 2017 International Workshop. We combined predictions from various models using an artificial neural network to determine the opinion towards an entity in (a) Microblog Messages and (b) News Headlines data. Our models achieved a cosine similarity score of 0.751 and 0.697 for the above two tracks giving us the rank of 2nd and 7th best team respectively.
UIT-DANGNT-CLNLP at SemEval-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving CAMR
Authors: Khoa Nguyen, Dang Nguyen
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Articles

This paper describes the improvements that we have applied on CAMR baseline parser (Wang et al., 2016) at Task 8 of SemEval-2016. Our objective is to increase the performance of CAMR when parsing sentences from scientific articles, especially articles of biology domain more accurately. To achieve this goal, we built two wrapper layers for CAMR. The first layer, which covers the input data, will normalize, add necessary information to the input sentences to make the input dependency parser and the aligner better handle reference citations, scientific figures, formulas, etc. The second layer, which covers the output data, will modify and standardize output data based on a list of scientific concept fixing patterns. This will help CAMR better handle biological concepts which are not in the training dataset. Finally, after applying our approach, CAMR has scored 0.65 F-score on the test set of Biomedical training data and 0.61 F-score on the official blind test dataset.
Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention
Authors: Jan Buys, Phil Blunsom
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: AMR Parsing, Decoder, Lemmatization, Sentence

We present a neural encoder-decoder AMR parser that extends an attention-based model by predicting the alignment between graph nodes and sentence tokens explicitly with a pointer mechanism. Candidate lemmas are predicted as a pre-processing step so that the lemmas of lexical concepts, as well as constant strings, are factored out of the graph linearization and recovered through the predicted alignments. The approach does not rely on syntactic parses or extensive external resources. Our parser obtained 59{\%} Smatch on the SemEval test set.
FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers
Authors: Simon Mille, Roberto Carlini, Alicia Burga, Leo Wanner
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentence

We present the contribution of Universitat Pompeu Fabra{'}s NLP group to the SemEval Task 9.2 (AMR-to-English Generation). The proposed generation pipeline comprises: (i) a series of rule-based graph-transducers for the syntacticization of the input graphs and the resolution of morphological agreements, and (ii) an off-the-shelf statistical linearization component.
RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation
Authors: Normunds Gruzitis, Didzis Gosko, Guntis Barzdins
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: AMR Parsing, AMR-to-Text Generation, BIG-bench Machine Learning, NER, Text Generation

By addressing both text-to-AMR parsing and AMR-to-text generation, SemEval-2017 Task 9 established AMR as a powerful semantic interlingua. We strengthen the interlingual aspect of AMR by applying the multilingual Grammatical Framework (GF) for AMR-to-text generation. Our current rule-based GF approach completely covered only 12.3{\%} of the test AMRs, therefore we combined it with state-of-the-art JAMR Generator to see if the combination increases or decreases the overall performance. The combined system achieved the automatic BLEU score of 18.82 and the human Trueskill score of 107.2, to be compared to the plain JAMR Generator results. As for AMR parsing, we added NER extensions to our SemEval-2016 general-domain AMR parser to handle the biomedical genre, rich in organic compound names, achieving Smatch F1=54.0{\%}.
PKU\_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge
Authors: Liang Wang, Sujian Li
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Chunking, Feature Engineering, General Classification, Information Retrieval, Keyphrase Extraction, Text Classification, Word Embeddings

This paper presents a system that participated in SemEval 2017 Task 10 (subtask A and subtask B): Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our proposed approach utilizes external knowledge to enrich feature representation of candidate keyphrase, including Wikipedia, IEEE taxonomy and pre-trained word embeddings etc. Ensemble of unsupervised models, random forest and linear models are used for candidate keyphrase ranking and keyphrase type classification. Our system achieves the 3rd place in subtask A and 4th place in subtask B.
NTNU-1@ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields
Authors: Erwin Marsi, Utpal Kumar Sikdar, Cristina Marco, Biswanath Barik, Rune S{\ae}tre
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Dependency Parsing, Named Entity Recognition (NER), Relation Extraction

We present NTNU{'}s systems for Task A (prediction of keyphrases) and Task B (labelling as Material, Process or Task) at SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our approach relies on supervised machine learning using Conditional Random Fields. Our system yields a micro F-score of 0.34 for Tasks A and B combined on the test data. For Task C (relation extraction), we relied on an independently developed system described in (Barik and Marsi, 2017). For the full Scenario 1 (including relations), our approach reaches a micro F-score of 0.33 (5th place). Here we describe our systems, report results and discuss errors.
LABDA at SemEval-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the BANNER tool and the UMLS Semantic Network
Authors: Isabel Segura-Bedmar, Crist{\'o}bal Col{\'o}n-Ruiz, Paloma Mart{\'\i}nez
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Articles, General Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper describes the system presented by the LABDA group at SemEval 2017 Task 10 ScienceIE, specifically for the subtasks of identification and classification of keyphrases from scientific articles. For the task of identification, we use the BANNER tool, a named entity recognition system, which is based on conditional random fields (CRF) and has obtained successful results in the biomedical domain. To classify keyphrases, we study the UMLS semantic network and propose a possible linking between the keyphrase types and the UMLS semantic groups. Based on this semantic linking, we create a dictionary for each keyphrase type. Then, a feature indicating if a token is found in one of these dictionaries is incorporated to feature set used by the BANNER tool. The final results on the test dataset show that our system still needs to be improved, but the conditional random fields and, consequently, the BANNER system can be used as a first approximation to identify and classify keyphrases.
The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields
Authors: Lung-Hao Lee, Kuei-Ching Lee, Yuen-Hsien Tseng
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Multi-Task Learning

This study describes the design of the NTNU system for the ScienceIE task at the SemEval 2017 workshop. We use self-defined feature templates and multiple conditional random fields with extracted features to identify keyphrases along with categorized labels and their relations from scientific publications. A total of 16 teams participated in evaluation scenario 1 (subtasks A, B, and C), with only 7 teams competing in all sub-tasks. Our best micro-averaging F1 across the three subtasks is 0.23, ranking in the middle among all 16 submissions.
MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications
Authors: Sijia Liu, Feichen Shen, Vipin Chaudhary, Hongfang Liu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Classification, General Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Word Embeddings

In this paper, we present MayoNLP{'}s results from the participation in the ScienceIE share task at SemEval 2017. We focused on the keyphrase classification task (Subtask B). We explored semantic similarities and patterns of keyphrases in scientific publications using pre-trained word embedding models. Word Embedding Distance Pattern, which uses the head noun word embedding to generate distance patterns based on labeled keyphrases, is proposed as an incremental feature set to enhance the conventional Named Entity Recognition feature sets. Support vector machine is used as the supervised classifier for keyphrase classification. Our system achieved an overall F1 score of 0.67 for keyphrase classification and 0.64 for keyphrase classification and relation detection.
Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator
Authors: Roman Kern, Stefan Falk, Andi Rexha
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Information Retrieval, Reading Comprehension

This paper describes our participation in SemEval-2017 Task 10. We competed in Subtask 1 and 2 which consist respectively in identifying all the key phrases in scientific publications and label them with one of the three categories: Task, Process, and Material. These scientific publications are selected from Computer Science, Material Sciences, and Physics domains. We followed a supervised approach for both subtasks by using a sequential classifier (CRF - Conditional Random Fields). For generating our solution we used a web-based application implemented in the EU-funded research project, named CODE. Our system achieved an F1 score of 0.39 for the Subtask 1 and 0.28 for the Subtask 2.
NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents
Authors: Biswanath Barik, Erwin Marsi
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: feature selection, General Classification, Knowledge Base Completion, Relation, Relation Extraction

This paper presents our relation extraction system for subtask C of SemEval-2017 Task 10: ScienceIE. Assuming that the keyphrases are already annotated in the input data, our work explores a wide range of linguistic features, applies various feature selection techniques, optimizes the hyper parameters and class weights and experiments with different problem formulations (single classification model vs individual classifiers for each keyphrase type, single-step classifier vs pipeline classifier for hyponym relations). Performance of five popular classification algorithms are evaluated for each problem formulation along with feature selection. The best setting achieved an F1 score of 71.0{\%} for synonym and 30.0{\%} for hyponym relation on the test data.