Articles

Complex word identification task focuses on identifying the difficult word from English sentence for a Non-Native speakers. Non- Native speakers are those who don’t have En- glish as their native language. It is a sub- task for lexical simplification. We have ex- perimented with word embedding features, or- thographic word …...

In this report we summarize the results of the SemEval 2016 Task 8: Meaning Representa- tion Parsing. Participants were asked to gener- ate Abstract Meaning Representation (AMR) (Banarescu et al., 2013) graphs for a set of English sentences in the news and discussion forum domains. Eleven sites submitted valid systems. …...

This paper describes the system that was sub- mitted to SemEval2016 Task 11: Complex Word Identification. It presents a preliminary investigation into exploring word difficulty for non-native English speakers. We developed two systems using Nearest Centroid Classifi- cation technique to distinguish complex words from simple words. Optimized over G-score, the …...

This paper presents the participation of the TALN team in the Complex Word Identifica- tion Task of SemEval-2016 (Task 11). The purpose of the task was to determine if a word in a given sentence can be judged as com- plex or not by a certain target audience. To experiment …...

This paper presents the MacSaar system de- veloped to identify complex words in English texts. MacSaar participated in the SemEval 2016 task 11: Complex Word Identification submitting two runs. The system is based on the assumption that complex words are likely to be less frequent and on average longer than …...

This paper describes aggregated learning models for Complex Word Identification (CWI) task in SemEval 2016. The work focused on selecting the features that de- termine complexity of words and used dif- ferent combinations of support vector ma- chine (SVM) and decision tree (DT) tech- niques for classification. These classifiers were …...

The complex word identification task refers to the process of identifying difficult words in a sentence from the perspective of readers be- longing to a specific target audience. This task has immense importance in the field of lexi- cal simplification. Lexical simplification helps in improving the readability of texts consist- …...

We present the description of the LTG entry in the SemEval-2016 Complex Word Identi- fication (CWI) task, which aimed to develop systems for identifying complex words in En- glish sentences. Our entry focused on the use of contextual language model features and the application of ensemble classification meth- ods. Both …...

This paper describes team MAZA entries for the 2016 SemEval Task 11: Complex Word Identification (CWI). The task is a binary clas- sification task in which systems are trained to predict whether a word in a sentence is con- sidered to be complex or not. We developed our two systems …...

We introduce the SV000gg systems: two En- semble Methods for the Complex Word Iden- tification task of SemEval 2016. While the SV000gg-Hard system exploits basic Hard Voting, the SV000gg-Soft system employs Performance-Oriented Soft Voting, which weights votes according to the voter’s perfor- mance rather than its prediction confidence, allowing for …...

SemEval 2016 task 11 involved determining whether words in a sentence were complex or simple for a cohort of people with English as a second language. Training data consisted of 200 annotated sentences, representing the combined judgements of 20 human annota- tors, such that if any annotator of the group …...

This paper describes an information-theoretic approach to complex word identification using a classifier based on an entropy based mea- sure based on word senses and sentence-level perplexity features. We describe the motiva- tion behind these features based on informa- tion density and demonstrate that they perform modestly well in the …...

This paper describes our submission to the Complex Word Identification (CWI) task in SemEval-2016. We test an experimental ap- proach to blindly use neural nets to solve the CWI task that we know little/nothing about. By structuring the input as a series of se- quences and the output as a …...

This paper presents the description of a sys- tem which detects complex words. It solely uses information regarding the presence of a word in a prepared vocabulary list. The sys- tem outperforms multiple more advanced sys- tems and is ranked fourth for the shared task, with minimal loss to the …...

This paper presents our system submitted for SemEval 2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiM- SUM; Schneider, Hovy, et al., 2016). We extend AMALGrAM (Schneider and Smith, 2015) by tapping two additional information sources. The first information source uses a semantic knowledge base (YAGO3; Suchanek et …...

We describe our entry to SemEval 2016 Task 10: Detecting Minimal Semantic Units and their Meanings. Our approach uses a discrim- inative first-order sequence model similar to Schneider and Smith (2015). The chief nov- elty in our approach is a factorization of the la- bels into multiword expression and supersense …...

This paper describes our approach towards the SemEval-2016 Task 10: Detecting Mini- mal Semantic Units and their Meanings (DiM- SUM). We consider that the two problems are similar to multiword expression detection and supersense tagging, respectively. The former problem is formalized as a sequence label- ing problem solved by first-order …...

The SemEval 2016 DiMSUM Shared Task concerns the detection of minimal semantic units from text and prediction of their coarse lexical categories known as supersenses. Our approach is to define this task as a binary clas- sification problem approachable by straight- forward machine learning methods. We start by detecting semantic …...

Community Question Answering (CAQ) sys- tems play an important role in people's lives due to the huge knowledge accumulated in them. In order to take full advantage of the huge knowledge, the target of semeval2016 task3 is to find the best answers to a new question in CQA. This work …...

We describe our system, ConvKN, participat- ing to the SemEval-2016 Task 3 “Commu- nity Question Answering”. The task targeted the reranking of questions and comments in real-life web fora both in English and Arabic. ConvKN combines convolutional tree kernels with convolutional neural networks and ad- ditional manually designed features including …...