Choosing the best word or phrase for a given context from among the candidate near-synonyms, such as slim and skinny, is a difficult language generation problem. In this paper, we describe approaches to solving an instance of this problem, the lexical gap problem, with a particular focus on affect and subjectivity; to do this we draw upon techniques from the sentiment and subjectivity analysis fields. We present a supervised approach to this problem, initially with a unigram model that solidly outperforms the baseline, with a 6.8% increase in accuracy. The results to some extent confirm those from related problems, where feature presence outperforms feature frequency, and immediate context features generally outperform wider context features. However, this latter is somewhat surprisingly not always the case, and not necessarily where intuition might first suggest; and an analysis of where document-level models are in some cases better suggested that, in our corpus, broader features related to the ‘tone’ of the document could be useful, including document sentiment, document author, and a distance metric for weighting the wider lexical context of the gap itself. From these, our best model has a 10.1% increase in accuracy, corresponding to a 38% reduction in errors. Moreover, our models do not just improve accuracy on affective word choice, but on non-affective word choice also.

Graph structures naturally model connections. In natural language processing (NLP) connections are ubiquitous, on anything between small and web scale. We find them between words – as grammatical, collocation or semantic relations – contributing to the overall meaning, and maintaining the cohesive structure of the text and the discourse unity. We find them between concepts in ontologies or other knowledge repositories – since the early ages of artificial intelligence, associative or semantic networks have been proposed and used as knowledge stores, because they naturally capture the language units and relations between them, and allow for a variety of inference and reasoning processes, simulating some of the functionalities of the human mind. We find them between complete texts or web pages, and between entities in a social network, where they model relations at the web scale. Beyond the more often encountered ‘regular’ graphs, hypergraphs have also appeared in our field to model relations between more than two units.

We consider automated generation of humorous texts by substitution of a single word in a given short text. In this setting, several factors that potentially contribute to the funniness of texts can be integrated into a unified framework as constraints on the lexical substitution. We discuss three types of such constraints: formal constraints concerning the similarity of sounds or spellings between the original word and the substitute, semantic or connotational constraints requiring the substitute to be a taboo word, and contextual constraints concerning the position and context of the replacement. Empirical evidence from extensive user studies using real SMSs as the corpus indicates that taboo constraints are statistically very effective, and so is a constraint requiring that the substitution takes place at the end of the text even though the effect is smaller. The effects of individual constraints are largely cumulative. In addition, connotational taboo words and word position have a strong interaction.

In this paper, we propose an unsupervised and automated method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books and millions of tweets posted per day. We construct distributional-thesauri-based networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the different time points. Subsequently, we propose a split/join based approach to compare the sense clusters at two different time points to find if there is ‘birth’ of a new sense. The approach also helps us to find if an older sense was ‘split’ into more than one sense or a newer sense has been formed from the ‘join’ of older senses or a particular sense has undergone ‘death’. We use this completely unsupervised approach (a) within the Google books data to identify word sense differences within a media, and (b) across Google books and Twitter data to identify differences in word sense distribution across different media. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet.

This work presents the development and evaluation of an extended Urdu parser. It further focuses on issues related to this parser and describes the changes made in the Earley algorithm to get accurate and relevant results from the Urdu parser. The parser makes use of a morphologically rich context free grammar extracted from a linguistically-rich Urdu treebank. This grammar with sufficient encoded information is comparable with the state-of-the-art parsing requirements for the morphologically rich Urdu language. The extended parsing model and the linguistically rich extracted-grammar both provide us better evaluation results in Urdu/Hindi parsing domain. The parser gives 87% of f-score, which outperforms the existing parsing work of Urdu/Hindi based on the tree-banking approach.

In this paper, we present a new method based on co-occurrence graphs for performing Cross-Lingual Word Sense Disambiguation (CLWSD). The proposed approach comprises the automatic generation of bilingual dictionaries, and a new technique for the construction of a co-occurrence graph used to select the most suitable translations from the dictionary. Different algorithms that combine both the dictionary and the co-occurrence graph are then used for performing this selection of the final translations: techniques based on sub-graphs (communities) containing clusters of words with related meanings, based on distances between nodes representing words, and based on the relative importance of each node in the whole graph. The initial output of the system is enhanced with translation probabilities, provided by a statistical bilingual dictionary. The system is evaluated using datasets from two competitions: task 3 of SemEval 2010, and task 10 of SemEval 2013. Results obtained by the different disambiguation techniques are analysed and compared to those obtained by the systems participating in the competitions. Our system offers the best results in comparison with other unsupervised systems in most of the experiments, and even overcomes supervised systems in some cases.

The emergence of knowledge repositories in a variety of domains provides a valuable opportunity for semantic interpretation of high dimensional datasets. Previous researches investigate the use of concept instead of word as a core semantic feature for incorporating semantic knowledge from an ontology into the representation model of documents. On the other hand, in machine learning and information retrieval, data objects are represented as a flat feature vector. The inconsistency between the structural nature of the knowledge repositories and the flat representation of features in machine learning leads researchers to neglect the structure of the knowledge base and leverage concepts as isolated semantic features, which is known as bag-of-concepts. Although, using concepts has some advantages over words, by neglecting the relation between concepts, the problem of vocabulary mismatch remains in force. In this paper, a novel semantic kernel is proposed which is capable of incorporating the relatedness between conceptual features. This kernel leverages clique theory to map data objects to a novel feature space wherein complex data objects will be comparable. The proposed kernel is relevant to all applications which have a prior knowledge about the relatedness between features. We concentrate on representing text documents and words using Wikipedia and WordNet, respectively. The experimental results over a set of benchmark datasets have revealed that the proposed kernel significantly improves the representation of both words and texts in the application of semantic relatedness.