Predicting worker disagreement for more effective crowd labeling

Crowdsourcing is a popular mechanism used for labeling tasks to produce large corpora for training. However, producing a reliable crowd labeled training corpus is challenging and resource consuming. Research on crowdsourcing has shown that label quality is much affected by worker engagement and expertise. In this study, we postulate that label quality can also be affected by inherent ambiguity of the documents to be labeled. Such ambiguities are not known in advance, of course, but, once encountered by the workers, they lead to disagreement in the labeling – a disagreement that cannot be resolved by employing more workers. To deal with this problem, we propose a crowd labeling framework: we train a disagreement predictor on a small seed of documents, and then use this predictor to decide which documents of the complete corpus should be labeled and which should be checked for document-inherent ambiguities before assigning (and potentially wasting) worker effort on them. We report on the findings of the experiments we conducted on crowdsourcing a Twitter corpus for sentiment classification

Delving into the uncharted territories of Word Sense Disambiguation

The automatic disambiguation of word senses, i.e. Word Sense Disambiguation, is a long-standing task in the field of Natural Language Processing; an AI-complete problem that took its first steps more than half a century ago, and which, to date, has apparently attained human-like performances on standard evaluation benchmarks. Unfortunately, the steady evolution that the task experienced over time in terms of sheer performance has not been followed hand in hand by adequate theoretical support, nor by careful error analysis. Furthermore, we believe that the lack of an exhaustive bird’s eye view which accounts for the sort of high-end and unrealistic computational architectures that systems will soon need in order to further refine their performances could lead the field to a dead angle in a few years. In essence, taking advantage of the current moment of great accomplishments and renewed interest in the task, we argue that Word Sense Disambiguation is mature enough for researchers to really observe the extent of the results hitherto obtained, evaluate what is actually missing, and answer the much sought for question: “are current state-of-the-art systems really able to effectively solve lexical ambiguity?” Driven by the desire to become both architects and participants in this period of pondering, we have identified a few macro-areas representatives of the challenges of automatic disambiguation. From this point of view, in this thesis, we propose experimental solutions and empirical tools so as to bring to the attention of the Word Sense Disambiguation community unusual and unexplored points of view. We hope these will represent a new perspective through which to best observe the current state of disambiguation, as well as to foresee future paths for the task to evolve on. Specifically, 1q) prompted by the growing concern about the rise in performance being closely linked to the demand for more and more unrealistic computational architectures in all areas of application of Deep Learning related techniques, we 1a) provide evidence for the undisclosed potential of approaches based on knowledge-bases, via the exploitation of syntagmatic information. Moreover, 2q) driven by the dissatisfaction with the use of cognitively-inaccurate, finite inventories of word senses in Word Sense Disambiguation, we 2a) introduce an approach based on Definition Modeling paradigms to generate contextual definitions for target words and phrases, hence going beyond the limits set by specific lexical-semantic inventories. Finally, 3q) moved by the desire to analyze the real implications beyond the idea of “machines performing disambiguation on par with their human counterparts” we 3a) put forward a detailed analysis of the shared errors affecting current state-of-the-art systems based on diverse approaches for Word Sense Disambiguation, and highlight, by means of a novel evaluation dataset tailored to represent common and critical issues shared by all systems, performances way lower than those usually reported in the current literature

Unsupervised methods to predict example difficulty in word sense annotation

[EU]Hitzen Adiera Desanbiguazioa (HAD) Hizkuntzaren Prozesamenduko (HP) erronkarik handienetakoa da. Frogatu denez, HAD sistema ahalik eta arrakastatsuenak entrenatzeko, oso garrantzitsua da entrenatze-datuetatik adibide (hitzen testuinguru) zailak kentzea, honela emaitzak asko hobetzen baitira. Lan honetan, lehenik, gainbegiratutako ereduak aztertzen ditugu, eta, ondoren, gainbegiratu gabeko bi neurri proposatzen ditugu. Gainbegiratutako ereduetan, adibideen zailtasuna definitzeko, anotatutako corpuseko datuak erabiltzen dira. Proposatzen ditugun bi gainbegiratu gabeko neurrietan, berriz, batetik, aztergai den hitzaren zailtasuna neurtzen da (hitzon Wordnet-eko datuak aztertuta), eta, bestetik, hitzaren agerpenarena (alegia, hitzaren testuinguruarena edo adibidearena). Biak konbinatuta, adibideen zailtasuna ezaugarritzeko eredu bat ere proposatzen da.[EN]Word Sense Disambiguation (WSD) is one of the major challenges in Natural Language Processing (NLP). In order to train successful WSD systems, it has been proved that removing difficult examples (words in a context) from the training set improves the performance of these systems. In this work, we first analyze supervised models that, given annotated data, characterize the difficulty of examples. We then propose two unsupervised measures to characterize the difficulty of target words (by analyzing their WordNet data) and occurrences (context sentences), respectively. Combining them, a model able to characterize the difficulty of examples is also presented