We introduce a new task, visual sense disambiguation for verbs: given an image and a verb, assign the correct sense of the verb, i.e., the one that describes the action depicted in the image. Just as textual word sense disambiguation is useful for a wide range of NLP tasks, visual sense disambiguation can be useful for multimodal tasks such as image retrieval, image description, and text illustration. We introduce VerSe, a new dataset that augments existing multimodal datasets (COCO and TUHOI) with sense labels. We propose an unsupervised algorithm based on Lesk which performs visual sense disambiguation using textual, visual, or multimodal embeddings. We find that textual embeddings perform well when gold-standard textual annotations (object labels and image descriptions) are available, while multimodal embeddings perform well on unannotated images. We also verify our findings by using the textual and multimodal embeddings as features in a supervised setting and analyse the performance of visual sense disambiguation task. VerSe is made publicly available and can be downloaded at: this https URL
There is an extensive literature on word sense disambiguation for nouns, verbs, adjectives and adverbs. Most of these approaches rely on lexical databases or sense inventories such as WordNet @cite_27 or OntoNotes @cite_28 . Unsupervised WSD approaches often rely on distributional representations, computed over the target word and its context @cite_7 @cite_11 @cite_5 . Most supervised approaches use sense annotated corpora to extract linguistic features of the target word (context words, POS tags, collocation features), which are then fed into a classifier to disambiguate test data @cite_6 . Recently, features based on sense-specific semantic vectors learned using large corpora and a sense inventory such as WordNet have been shown to achieve state-of-the-art results for supervised WSD @cite_32 @cite_24 .
@cite_7 @cite_28 @cite_32 @cite_6 @cite_24 @cite_27 @cite_5 @cite_11
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A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning. In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task. Our investigation reveals that this method of acquiring sense-tagged data is promising. On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0 , and the difference could narrow further to 6.5 if we disregard the advantage that manually sense-tagged data have in their sense coverage. Our analysis also highlights the importance of the issue of domain dependence in evaluating WSD programs. In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The problem with using the predominant, or first sense heuristic, aside from the fact that it does not take surrounding context into account, is that it assumes some quantity of hand-tagged data. Whilst there are a few hand-tagged corpora available for some languages, one would expect the frequency distribution of the senses of words, particularly topical words, to depend on the genre and domain of the text under consideration. We present work on the use of a thesaurus acquired from raw textual corpora and the WordNet similarity package to find predominant noun senses automatically. The acquired predominant senses give a precision of 64 on the nouns of the SENSEVAL-2 English all-words task. This is a very promising result given that our method does not require any hand-tagged text, such as SemCor. Furthermore, we demonstrate that our method discovers appropriate predominant senses for words from two domain-specific corpora. The current situation for Word Sense Disambiguation (WSD) is somewhat stuck due to lack of training data. We present in this paper a novel disambiguation algorithm that improves previous systems based on acquisition of examples by incorporating local context information. With a basic configuration, our method is able to obtain state-of-the-art performance. We complemented this work by evaluating other well-known methods in the same dataset, and analysing the comparative results per word. We observed that each algorithm performed better for different types of words, and each of them failed for some particular words. We proposed then a simple unsupervised voting scheme that improved significantly over single systems, achieving the best unsupervised performance on both the Senseval 2 and Senseval 3 lexical sample datasets. In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neighboring words, morphological form, the unordered set of surrounding words, local collocations, and verb-object syntactic relation. We tested our WSD program, named LEXAS, on both a common data set used in previous work, as well as on a large sense-tagged corpus that we separately constructed. LEXAS achieves a higher accuracy on the common data set, and performs better than the most frequent heuristic on the highly ambiguous words in the large corpus tagged with the refined senses of WORDNET.