ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing

In this paper, we present a novel unsupervised algorithm for word sense disambiguation (WSD) at the document level. Our algorithm is inspired by a widely-used approach in the field of genetics for whole genome sequencing, known as the Shotgun sequencing technique. The proposed WSD algorithm is based on three main steps. First, a brute-force WSD algorithm is applied to short context windows (up to 10 words) selected from the document in order to generate a short list of likely sense configurations for each window. In the second step, these local sense configurations are assembled into longer composite configurations based on suffix and prefix matching. The resulted configurations are ranked by their length, and the sense of each word is chosen based on a voting scheme that considers only the top k configurations in which the word appears. We compare our algorithm with other state-of-the-art unsupervised WSD algorithms and demonstrate better performance, sometimes by a very large margin. We also show that our algorithm can yield better performance than the Most Common Sense (MCS) baseline on one data set. Moreover, our algorithm has a very small number of parameters, is robust to parameter tuning, and, unlike other bio-inspired methods, it gives a deterministic solution (it does not involve random choices).Comment: In Proceedings of EACL 201

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

International audienceWord 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

TOWARDS BUILDING A WORDNET NOUN ONTOLOGY

Abstract. 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 work describes an attempt to draw this distinction and reports the way in which the results were incorporated in the last version (2.1) of WordNet

An Evaluation Exercise for Romanian Word Sense Disambiguation

This paper discusses an evaluation exercise for Romanian word sense disambiguation

On the Semiautomatic Generation of WordNet Type Synsets and Clusters

WordNet (WN) is a lexical knowledge base, first developed for English and then adopted for several Western European languages, which was created as a machine-readable dictionary based on psycholinguistic principles. Our paper is an attempt to discuss the semiautomatic generation of WNs for languages other than English, a topic of great interest since the existence of such WNs will create the appropriate infrastructure for advanced Information Technology systems. Extending the algorithmic approach proposed in [Nikolov and Petrova, 01] we introduce a semiautomatic method based on heuristics for generating noun and adjective synsets and clusters. This choice of involved parts of speech is determined by the fact that nouns and adjectives have completely different organizations in WN: the hierarchy and the N-dimensional hyper-space respectively. Our approach to WN generation relies on so-called "class methods", namely it uses as knowledge sources individual entries coming from bilingual dictio� naries and WN synsets, but at the same time demonstrates the need to combine such methods with structural ones

Towards a Benchmarking System for Comparing Automatic Hate Speech Detection with an Intelligent Baseline Proposal

Hate Speech is a frequent problem occurring among Internet users. Recent regulations are being discussed by U.K. representatives (“Online Safety Bill”) and by the European Commission, which plans on introducing Hate Speech as an “EU crime”. The recent legislation having passed in order to combat this kind of speech places the burden of identification on the hosting websites and often within a tight time frame (24 h in France and Germany). These constraints make automatic Hate Speech detection a very important topic for major social media platforms. However, recent literature on Hate Speech detection lacks a benchmarking system that can evaluate how different approaches compare against each other regarding the prediction made concerning different types of text (short snippets such as those present on Twitter, as well as lengthier fragments). This paper intended to deal with this issue and to take a step forward towards the standardization of testing for this type of natural language processing (NLP) application. Furthermore, this paper explored different transformer and LSTM-based models in order to evaluate the performance of multi-task and transfer learning models used for Hate Speech detection. Some of the results obtained in this paper surpassed the existing ones. The paper concluded that transformer-based models have the best performance on all studied Datasets

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

International audienceWhether 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 bene cial 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 bene ts 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

WordNet nouns: Classes and instances

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. If you were to say “Women are numerous, ” you would not wish to imply that any particular woman is numerous. Instead, you would probably mean something like “The class of women contains numerous instances. ” To say, on the other hand, “Rosa Parks is numerous, ” would be nonsense. Whereas the noun woman denotes a class, the proper noun Rosa Parks is an instance of that class. As Quirk et al. (1985, page 288) point out, proper nouns normally lack number contrast. This important distinction between classes and instances underlies the present discussion of WordNet nouns. Some nouns are understood to refer to classes; membership in those classes determines the semantic relation of hyponymy that is basic for the organization of nouns in WordNet (WN). Other nouns, however, are understood to refer to particular individuals. In many cases the distinction is clear, but not always