RegressionModel
Related Papers
Ranking Reader Emotions Using Pairwise Loss Minimization and Emotional Distribution Regression
This paper presents two approaches to ranking reader emotions of documents. Past studies assign a document to a single emotion cate- gory, so their methods cannot be applied di- rectly …...
A Hybrid Hierarchical Model for Multi-Document Summarization
Scoring sentences in documents given ab- stract summaries created by humans is im- portant in extractive multi-document sum- marization. In this paper, we formulate ex- tractive summarization as a two …...
Not All Seeds Are Equal: Measuring the Quality of Text Mining Seeds
Open-class semantic lexicon induction is of great interest for current knowledge harvest- ing algorithms. We propose a general frame- work that uses patterns in bootstrapping fash- ion to learn open-class …...
Using Supervised Bigram-based ILP for Extractive Summarization
In this paper, we propose a bigram based supervised method for extractive docu- ment summarization in the integer linear programming (ILP) framework. For each bigram, a regression model is used …...
Looking at Unbalanced Specialized Comparable Corpora for Bilingual Lexicon Extraction
The main work in bilingual lexicon ex- traction from comparable corpora is based on the implicit hypothesis that corpora are balanced. However, the historical context- based projection method dedicated to …...
SSMT:A Machine Translation Evaluation View To Paragraph-to-Sentence Semantic Similarity
This paper presents the system SSMT measuring the semantic similarity between a paragraph and a sentence submitted to the SemEval 2014 task3: Cross-level Se- mantic Similarity. The special difficulty of …...
DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity SemEval-2015 Task 2
We describe the work carried out by the DCU team on the Semantic Textual Similarity task at SemEval-2015. We learn a regression model to predict a semantic similarity score between …...
ECNU: Multi-level Sentiment Analysis on Twitter Using Traditional Linguistic Features and Word Embedding Features
This paper reports our submission to task 10 (Sentiment Analysis on Tweet, SAT) (Rosen- thal et al., 2015) in SemEval 2015 , which contains five subtasks, i.e., contextual polar- ity …...
INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction
We present the approach followed by INESC- ID in the SemEval 2015 Twitter Sentiment Analysis challenge, subtask E. The goal was to determine the strength of the association of Twitter …...
The Rating Game: Sentiment Rating Reproducibility from Text
Sentiment analysis models often use rat- ings as labels, assuming that these rat- ings reflect the sentiment of the accom- panying text. We investigate (i) whether human readers can infer …...
Assessing the Prosody of Non-Native Speakers of English: Measures and Feature Sets
AbstractIn this paper, we describe a new database with audio recordings of non-native (L2) speakers of English, and the perceptual evaluation experiment conducted with native English speakers for assessing the …...
PMI-cool at SemEval-2016 Task 3: Experiments with PMI and Goodness Polarity Lexicons for Community Question Answering
We describe our submission to SemEval-2016 Task 3 on Community Question Answering. We participated in subtask A, which asks to rerank the comments from the thread for a given forum …...
Syndromic Surveillance using Generic Medical Entities on Twitter
Public health surveillance is challenging due to difficulties accessing medical data in real-time. We present a novel, ef- fective and computationally inexpensive method for syndromic surveillance using Twitter data. The …...
An Empirical Analysis of Edit Importance between Document Versions
AbstractIn this paper, we present a novel approach to infer significance of various textual edits to documents. An author may make several edits to a document; each edit varies in …...
Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data
AbstractCrowdsourcing offers a convenient means of obtaining labeled data quickly and inexpensively. However, crowdsourced labels are often noisier than expert-annotated data, making it difficult to aggregate them meaningfully. We present …...
INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines
AbstractThis 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 …...
Cross-media User Profiling with Joint Textual and Social User Embedding
AbstractIn realistic scenarios, a user profiling model (e.g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to …...
Buhscitu at SemEval-2020 Task 7: Assessing Humour in Edited News Headlines Using Hand-Crafted Features and Online Knowledge Bases
AbstractThis paper describes a system that aims at assessing humour intensity in edited news headlines as part of the 7th task of SemEval-2020 on “Humor, Emphasis and Sentiment”. Various factors …...
Predicting Performance for Natural Language Processing Tasks
AbstractGiven the complexity of combinations of tasks, languages, and domains in natural language processing (NLP) research, it is computationally prohibitive to exhaustively test newly proposed models on each possible experimental …...