http://wwwusers.di.uniroma1.it/~navigli/pubs/AIJ_2012_Navigli_Ponzetto.pdf

BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network

https://www.cs.tufts.edu/comp/150AIH/pdf/NadkarniOC11.pdf

Natural language processing: an introduction.

We take the category system in Wikipedia as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexicosyntactic matching. As a result we are able to derive a large scale taxonomy containing a large amount of subsumption, i.e. isa, relations. We evaluate the quality of the created resource by comparing it with ResearchCyc, one of the largest manually annotated ontologies, as well as computing semantic similarity between words in benchmarking datasets.

/pdf/deriving-a-large-scale-taxonomy-from-wikipedia-52otqbprdf.pdf

Deriving a large scale taxonomy from Wikipedia

Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training by a word expert for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features.

/pdf/neural-sequence-learning-models-for-word-sense-3us9umji8m.pdf

Neural Sequence Learning Models for Word Sense Disambiguation

Automatic vehicle classification is crucial to intelligent transportation system, especially for vehicle-tracking by police. Due to the complex lighting and image capture conditions, image-based vehicle classification in real-world environments is still a challenging task and the performance is far from being satisfactory. However, owing to the mechanism of visual attention, the human vision system shows remarkable capability compared with the computer vision system, especially in distinguishing nuances processing. Inspired by this mechanism, we propose a convolutional neural network (CNN) model of visual attention for image classification. A visual attention-based image processing module is used to highlight one part of an image and weaken the others, generating a focused image. Then the focused image is input into the CNN to be classified. According to the classification probability distribution, we compute the information entropy to guide a reinforcement learning agent to achieve a better policy for image classification to select the key parts of an image. Systematic experiments on a surveillance-nature dataset which contains images captured by surveillance cameras in the front view, demonstrate that the proposed model is more competitive than the large-scale CNN in vehicle classification tasks.

Deep Reinforcement Learning With Visual Attention for Vehicle Classification

WordNet, a lexical database for English that is extensively used by computational linguists, has not previously distinguished hyponyms that are classes from hyponyms that are instances. This note describes an attempt to draw that distinction and proposes a simple way to incorporate the results into future versions of WordNet.

/pdf/wordnet-nouns-classes-and-instances-3gpt7rx1nb.pdf

WordNet Nouns: Classes and Instances

Statistical Natural Language Processing.

The present paper concentrates on the issue of feature selection for unsupervised word sense disambiguation (WSD) performed with an underlying Naive Bayes model. It introduces dependency-based feature selection which, to our knowledge, is used for the first time in conjunction with the Naive Bayes model acting as clustering technique. Construction of the dependency-based semantic space required for the proposed task is discussed. The resulting disambiguation method, representing an extension of the method introduced in [15], lies at the border between unsupervised and knowledge-based techniques. Syntactic knowledge provided by dependency relations (and exemplified in the case of adjectives) is hereby compared to semantic knowledge offered by the semantic network WordNet (and examined in [15]). Our conclusion is that the Naive Bayes model reacts well in the presence of syntactic knowledge of this type and that dependency-based feature selection is a reliable alternative to the WordNet-based semantic one.

Feeding Syntactic Versus Semantic Knowledge to a Knowledge-lean Unsupervised Word Sense Disambiguation Algorithm with an Underlying Naïve Bayes Model

Word sense ambiguity has been identified as a cause of poor precision in information retrieval (IR) systems. Word sense disambiguation and discrimination methods have been defined to help systems choose which documents should be retrieved in relation to an ambiguous query. However, the only approaches that show a genuine benefit for word sense discrimination or disambiguation in IR are generally supervised ones. In this paper we propose a new unsupervised method that uses word sense discrimination in IR. The method we develop is based on spectral clustering and reorders an initially retrieved document list by boosting documents that are semantically similar to the target query. For several TREC ad hoc collections we show that our method is useful in the case of queries which contain ambiguous terms. We are interested in improving the level of precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30) respectively. We show that precision can be improved by 8% above current state-of-the-art baselines. We also focus on poor performing queries.

/pdf/word-sense-discrimination-in-information-retrieval-a-4xceu7tx9r.pdf

Word sense discrimination in information retrieval: A spectral clustering-based approach

This paper aims to fully present a new word sense disambiguation method that has been introduced in Hristea and Popescu (Fundam Inform 91(3---4):547---562, 2009) and so far tested in the case of adjectives (Hristea and Popescu in Fundam Inform 91(3---4):547---562, 2009) and verbs (Hristea in Int Rev Comput Softw 4(1):58---67, 2009). We hereby extend the method to the case of nouns and draw conclusions regarding its performance with respect to all these parts of speech. The method lies at the border between unsupervised and knowledge-based techniques. It performs unsupervised word sense disambiguation based on an underlying Naive Bayes model, while using WordNet as knowledge source for feature selection. The performance of the method is compared to that of previous approaches that rely on completely different feature sets. Test results for all involved parts of speech show that feature selection using a knowledge source of type WordNet is more effective in disambiguation than local type features (like part-of-speech tags) are.

Florentina Hristea

Papers

WordNet Nouns: Classes and Instances

Statistical Natural Language Processing.

Feeding Syntactic Versus Semantic Knowledge to a Knowledge-lean Unsupervised Word Sense Disambiguation Algorithm with an Underlying Naïve Bayes Model

Word sense discrimination in information retrieval: A spectral clustering-based approach

Performing word sense disambiguation at the border between unsupervised and knowledge-based techniques