scholarly journals It’s All Fun and Games until Someone Annotates: Video Games with a Purpose for Linguistic Annotation

2014 ◽  
Vol 2 ◽  
pp. 449-464 ◽  
Author(s):  
David Jurgens ◽  
Roberto Navigli
Keyword(s):  
Video Games ◽  
Large Scale ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Current State ◽  
Linguistic Annotation ◽  
Games With A Purpose ◽  
Sense Disambiguation ◽  
Major Bottleneck ◽  
Annotation Quality

Annotated data is prerequisite for many NLP applications. Acquiring large-scale annotated corpora is a major bottleneck, requiring significant time and resources. Recent work has proposed turning annotation into a game to increase its appeal and lower its cost; however, current games are largely text-based and closely resemble traditional annotation tasks. We propose a new linguistic annotation paradigm that produces annotations from playing graphical video games. The effectiveness of this design is demonstrated using two video games: one to create a mapping from WordNet senses to images, and a second game that performs Word Sense Disambiguation. Both games produce accurate results. The first game yields annotation quality equal to that of experts and a cost reduction of 73% over equivalent crowdsourcing; the second game provides a 16.3% improvement in accuracy over current state-of-the-art sense disambiguation games with WordNet.

Word Sense Disambiguation

2013 ◽  
pp. 22-51
Author(s):  
Pushpak Bhattacharyya ◽  
Mitesh Khapra
Keyword(s):  
State Of The Art ◽  
Word Sense Disambiguation ◽  
Current Trend ◽  
General Purpose ◽  
Word Sense ◽  
Domain Specific ◽  
Knowledge Based ◽  
Current State ◽  
Sense Disambiguation ◽  
State Of Affairs

This chapter discusses the basic concepts of Word Sense Disambiguation (WSD) and the approaches to solving this problem. Both general purpose WSD and domain specific WSD are presented. The first part of the discussion focuses on existing approaches for WSD, including knowledge-based, supervised, semi-supervised, unsupervised, hybrid, and bilingual approaches. The accuracy value for general purpose WSD as the current state of affairs seems to be pegged at around 65%. This has motivated investigations into domain specific WSD, which is the current trend in the field. In the latter part of the chapter, we present a greedy neural network inspired algorithm for domain specific WSD and compare its performance with other state-of-the-art algorithms for WSD. Our experiments suggest that for domain-specific WSD, simply selecting the most frequent sense of a word does as well as any state-of-the-art algorithm.

scholarly journals A Large-Scale Pseudoword-Based Evaluation Framework for State-of-the-Art Word Sense Disambiguation

2014 ◽  
Vol 40 (4) ◽  
pp. 837-881 ◽  
Author(s):  
Mohammad Taher Pilehvar ◽  
Roberto Navigli
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Word Sense Disambiguation ◽  
Evaluation Framework ◽  
Small Scale ◽  
Word Sense ◽  
Knowledge Based ◽  
Depth Analysis ◽  
Sense Disambiguation ◽  
The Impact

The evaluation of several tasks in lexical semantics is often limited by the lack of large amounts of manual annotations, not only for training purposes, but also for testing purposes. Word Sense Disambiguation (WSD) is a case in point, as hand-labeled datasets are particularly hard and time-consuming to create. Consequently, evaluations tend to be performed on a small scale, which does not allow for in-depth analysis of the factors that determine a systems' performance. In this paper we address this issue by means of a realistic simulation of large-scale evaluation for the WSD task. We do this by providing two main contributions: First, we put forward two novel approaches to the wide-coverage generation of semantically aware pseudowords (i.e., artificial words capable of modeling real polysemous words); second, we leverage the most suitable type of pseudoword to create large pseudosense-annotated corpora, which enable a large-scale experimental framework for the comparison of state-of-the-art supervised and knowledge-based algorithms. Using this framework, we study the impact of supervision and knowledge on the two major disambiguation paradigms and perform an in-depth analysis of the factors which affect their performance.

scholarly journals Exemplification Modeling: Can You Give Me an Example, Please?

2021 ◽  
Author(s):  
Edoardo Barba ◽  
Luigi Procopio ◽  
Caterina Lacerra ◽  
Tommaso Pasini ◽  
Roberto Navigli
Keyword(s):  
Gold Standard ◽  
State Of The Art ◽  
Word Sense Disambiguation ◽  
Full Range ◽  
Training Data ◽  
Training Procedure ◽  
Word Sense ◽  
The Novel ◽  
Current State ◽  
Sense Disambiguation

Recently, generative approaches have been used effectively to provide definitions of words in their context. However, the opposite, i.e., generating a usage example given one or more words along with their definitions, has not yet been investigated. In this work, we introduce the novel task of Exemplification Modeling (ExMod), along with a sequence-to-sequence architecture and a training procedure for it. Starting from a set of (word, definition) pairs, our approach is capable of automatically generating high-quality sentences which express the requested semantics. As a result, we can drive the creation of sense-tagged data which cover the full range of meanings in any inventory of interest, and their interactions within sentences. Human annotators agree that the sentences generated are as fluent and semantically-coherent with the input definitions as the sentences in manually-annotated corpora. Indeed, when employed as training data for Word Sense Disambiguation, our examples enable the current state of the art to be outperformed, and higher results to be achieved than when using gold-standard datasets only. We release the pretrained model, the dataset and the software at https://github.com/SapienzaNLP/exmod.

scholarly journals What Determines Inter-Coder Agreement in Manual Annotations? A Meta-Analytic Investigation

2011 ◽  
Vol 37 (4) ◽  
pp. 699-725 ◽  
Author(s):  
Petra Saskia Bayerl ◽  
Karsten Ingmar Paul
Keyword(s):  
Meta Analysis ◽  
Word Sense Disambiguation ◽  
Correct Choice ◽  
Word Sense ◽  
Analytic Investigation ◽  
Know How ◽  
Sense Disambiguation ◽  
Set Up ◽  
Annotation Quality ◽  
Practical Recommendations

Recent discussions of annotator agreement have mostly centered around its calculation and interpretation, and the correct choice of indices. Although these discussions are important, they only consider the “back-end” of the story, namely, what to do once the data are collected. Just as important in our opinion is to know how agreement is reached in the first place and what factors influence coder agreement as part of the annotation process or setting, as this knowledge can provide concrete guidelines for the planning and set-up of annotation projects. To investigate whether there are factors that consistently impact annotator agreement we conducted a meta-analytic investigation of annotation studies reporting agreement percentages. Our meta-analysis synthesized factors reported in 96 annotation studies from three domains (word-sense disambiguation, prosodic transcriptions, and phonetic transcriptions) and was based on a total of 346 agreement indices. Our analysis identified seven factors that influence reported agreement values: annotation domain, number of categories in a coding scheme, number of annotators in a project, whether annotators received training, the intensity of annotator training, the annotation purpose, and the method used for the calculation of percentage agreements. Based on our results we develop practical recommendations for the assessment, interpretation, calculation, and reporting of coder agreement. We also briefly discuss theoretical implications for the concept of annotation quality.

scholarly journals Word Sense Disambiguation Based on Large Scale Polish CLARIN Heterogeneous Lexical Resources

2015 ◽  
pp. 269-292 ◽  
Author(s):  
Paweł Kędzia ◽  
Maciej Piasecki ◽  
Marlena Orlińska
Keyword(s):  
Large Scale ◽  
Word Sense Disambiguation ◽  
Large Network ◽  
Word Sense ◽  
Lexical Resources ◽  
Page Rank ◽  
Language Engineering ◽  
Sense Disambiguation ◽  
Semantic Resources ◽  
Word Senses

Word Sense Disambiguation Based on Large Scale Polish CLARIN Heterogeneous Lexical ResourcesLexical resources can be applied in many different Natural Language Engineering tasks, but the most fundamental task is the recognition of word senses used in text contexts. The problem is difficult, not yet fully solved and different lexical resources provided varied support for it. Polish CLARIN lexical semantic resources are based on the plWordNet — a very large wordnet for Polish — as a central structure which is a basis for linking together several resources of different types. In this paper, several Word Sense Disambiguation (henceforth WSD) methods developed for Polish that utilise plWordNet are discussed. Textual sense descriptions in the traditional lexicon can be compared with text contexts using Lesk’s algorithm in order to find best matching senses. In the case of a wordnet, lexico-semantic relations provide the main description of word senses. Thus, first, we adapted and applied to Polish a WSD method based on the Page Rank. According to it, text words are mapped on their senses in the plWordNet graph and Page Rank algorithm is run to find senses with the highest scores. The method presents results lower but comparable to those reported for English. The error analysis showed that the main problems are: fine grained sense distinctions in plWordNet and limited number of connections between words of different parts of speech. In the second approach plWordNet expanded with the mapping onto the SUMO ontology concepts was used. Two scenarios for WSD were investigated: two step disambiguation and disambiguation based on combined networks of plWordNet and SUMO. In the former scenario, words are first assigned SUMO concepts and next plWordNet senses are disambiguated. In latter, plWordNet and SUMO are combined in one large network used next for the disambiguation of senses. The additional knowledge sources used in WSD improved the performance. The obtained results and potential further lines of developments were discussed.

scholarly journals Feature selection for spectral clustering: to help or not to help spectral clustering when performing sense discrimination for IR?

2018 ◽  
Vol 8 (1) ◽  
pp. 218-227 ◽  
Author(s):  
Adrian-Gabriel Chifu ◽  
Florentina Hristea
Keyword(s):  
Feature Selection ◽  
Spectral Clustering ◽  
Large Scale ◽  
Word Sense Disambiguation ◽  
Large Data ◽  
Feature Weighting ◽  
Word Sense ◽  
Sense Disambiguation ◽  
Selection For ◽  
Data Collections

Abstract Whether or not word sense disambiguation (WSD) can improve information retrieval (IR) results represents a topic that has been intensely debated over the years, with many inconclusive or contradictory conclusions. The most rarely used type of WSD for this task is the unsupervised one, although it has been proven to be beneficial at a large scale. Our study builds on existing research and tries to improve the most recent unsupervised method which is based on spectral clustering. It investigates the possible benefits of “helping” spectral clustering through feature selection when it performs sense discrimination for IR. Results obtained so far, involving large data collections, encourage us to point out the importance of feature selection even in the case of this advanced, state of the art clustering technique that is known for performing its own feature weighting. By suggesting an improvement of what we consider the most promising approach to usage of WSD in IR, and by commenting on its possible extensions, we state that WSD still holds a promise for IR and hope to stimulate continuation of this line of research, perhaps at an even more successful level.

scholarly journals The Knowledge Acquisition Bottleneck Problem in Multilingual Word Sense Disambiguation

2020 ◽  
Author(s):  
Tommaso Pasini
Keyword(s):  
Knowledge Acquisition ◽  
Language Processing ◽  
Large Scale ◽  
Semantic Information ◽  
Word Sense Disambiguation ◽  
Knowledge Bases ◽  
Word Sense ◽  
Future Directions ◽  
Sense Disambiguation ◽  
Made In

Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word in a given context. It lies at the base of Natural Language Processing as it provides semantic information for words. In the last decade, great strides have been made in this field and much effort has been devoted to mitigate the knowledge acquisition bottleneck problem, i.e., the problem of semantically annotating texts at a large scale and in different languages. This issue is ubiquitous in WSD as it hinders the creation of both multilingual knowledge bases and manually-curated training sets. In this work, we first introduce the reader to the task of WSD through a short historical digression and then take the stock of the advancements to alleviate the knowledge acquisition bottleneck problem. In that, we survey the literature on manual, semi-automatic and automatic approaches to create English and multilingual corpora tagged with sense annotations and present a clear overview over supervised models for WSD. Finally, we provide our view over the future directions that we foresee for the field.

scholarly journals Word Sense Disambiguation Using Prior Probability Estimation Based on the Korean WordNet

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2938
Author(s):  
Minho Kim ◽  
Hyuk-Chul Kwon
Keyword(s):  
Large Scale ◽  
Prior Probability ◽  
Word Sense Disambiguation ◽  
Probability Estimation ◽  
Word Sense ◽  
Occurrence Probability ◽  
Knowledge Based ◽  
Ambiguous Words ◽  
Lexical Disambiguation ◽  
Sense Disambiguation

Supervised disambiguation using a large amount of corpus data delivers better performance than other word sense disambiguation methods. However, it is not easy to construct large-scale, sense-tagged corpora since this requires high cost and time. On the other hand, implementing unsupervised disambiguation is relatively easy, although most of the efforts have not been satisfactory. A primary reason for the performance degradation of unsupervised disambiguation is that the semantic occurrence probability of ambiguous words is not available. Hence, a data deficiency problem occurs while determining the dependency between words. This paper proposes an unsupervised disambiguation method using a prior probability estimation based on the Korean WordNet. This performs better than supervised disambiguation. In the Korean WordNet, all the words have similar semantic characteristics to their related words. Thus, it is assumed that the dependency between words is the same as the dependency between their related words. This resolves the data deficiency problem by determining the dependency between words by calculating the χ2 statistic between related words. Moreover, in order to have the same effect as using the semantic occurrence probability as prior probability, which is used in supervised disambiguation, semantically related words of ambiguous vocabulary are obtained and utilized as prior probability data. An experiment was conducted with Korean, English, and Chinese to evaluate the performance of our proposed lexical disambiguation method. We found that our proposed method had better performance than supervised disambiguation methods even though our method is based on unsupervised disambiguation (using a knowledge-based approach).

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