Examining inter-sentential influences on predicted verb subcategorization

This study investigated the influences of prior discourse context and cumulative syntactic priming on readers' predictions for verb subcategorizations. An additional aim was to determine whether cumulative syntactic priming has the same degree of influence following coherent discourse contexts as when following series of unrelated sentences. Participants (N = 40) read sentences using a self-paced, sentence-by-sentence procedure. Half of these sentences comprised a coherent discourse context intended to increase the expectation for a sentential complement (S) completion. The other half consisted of scrambled sentences. The trials in both conditions varied according to the proportion of verbs that resolved to an S (either 6S or 2S). Following each condition, participants read temporarily ambiguous sentences that resolved to an S. Reading times across the disambiguating and postdisambiguating regions were measured. No significant main effects or interactions were found for either region. However, the lack of significant findings for these analyses may have been due to low power. In a follow-up analysis, data from each gender were analyzed separately. For the data contributed by males, there were no significant findings. For the data contributed by females, the effect of coherence was significant (by participants but not by items) across the postdisambiguating region, and there was a marginally significant interaction (p =.05) between coherence and frequency across this region suggesting that discourse-level information may differentially influence the local sentence processing of female and male participant

High WSD Accuracy Using Naive Bayesian Classifier with Rich Features

Word Sense Disambiguation (WSD) is the task of choosing the right sense of an ambiguous word given a context. Using Naive Bayesian (NB) classifiers is known as one of the best methods for supervised approaches for WSD (Mooney, 1996; Pedersen, 2000), and this model usually uses only a topic context represented by unordered words in a large context. In this paper, we show that by adding more rich knowledge, represented by ordered words in a local context and collocations, the NB classifier can achieve higher accuracy in comparison with the best previously published results. The features were chosen using a forward sequential selection algorithm. Our experiments obtained 92.3% accuracy for four common test words (interest, line, hard, serve). We also tested on a large dataset, the DSO corpus, and obtained accuracies of 66.4% for verbs and 72.7% for nouns

PowerMap: mapping the real semantic web on the fly

ISWC is the premier international conference in the field. This paper describes innovative work on dynamic mapping of heterogeneous knowledge structures, a fundamental enabling technology for the next generation of large-scale intelligent applications on the emerging semantic web. The ideas underlying this work have provided the scientific basis for two large EU FP6 projects, NeOn and OpenKnowledge (Prof. Motta co-ordinates the former and leads the OU contribution on the latter), worth £2M to The Open University. Each project was ranked first in its class. The work is situated in the context of a new paradigm for exploiting large scale semantics, which has been presented at invited keynotes at prestigious international fora, including the 1st Asian Semantic Web Conference (ASWC 2006) and the 5th International Conference on Language Resources and Evaluation (LREC 2006)

Knowledge-based Word Sense Disambiguation using Topic Models

Word Sense Disambiguation is an open problem in Natural Language Processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. Typically WSD systems use the sentence or a small window of words around the target word as the context for disambiguation because their computational complexity scales exponentially with the size of the context. In this paper, we leverage the formalism of topic model to design a WSD system that scales linearly with the number of words in the context. As a result, our system is able to utilize the whole document as the context for a word to be disambiguated. The proposed method is a variant of Latent Dirichlet Allocation in which the topic proportions for a document are replaced by synset proportions. We further utilize the information in the WordNet by assigning a non-uniform prior to synset distribution over words and a logistic-normal prior for document distribution over synsets. We evaluate the proposed method on Senseval-2, Senseval-3, SemEval-2007, SemEval-2013 and SemEval-2015 English All-Word WSD datasets and show that it outperforms the state-of-the-art unsupervised knowledge-based WSD system by a significant margin.Comment: To appear in AAAI-1

Embeddings for word sense disambiguation: an evaluation study

Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. In this work, we study how word embeddings can be used in Word Sense Disambiguation, one of the oldest tasks in Natural Language Processing and Artificial Intelligence. We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. We show how a WSD system that makes use of word embeddings alone, if designed properly, can provide significant performance improvement over a state-of-the-art WSD system that incorporates several standard WSD features

Combining Knowledge- and Corpus-based Word-Sense-Disambiguation Methods

In this paper we concentrate on the resolution of the lexical ambiguity that arises when a given word has several different meanings. This specific task is commonly referred to as word sense disambiguation (WSD). The task of WSD consists of assigning the correct sense to words using an electronic dictionary as the source of word definitions. We present two WSD methods based on two main methodological approaches in this research area: a knowledge-based method and a corpus-based method. Our hypothesis is that word-sense disambiguation requires several knowledge sources in order to solve the semantic ambiguity of the words. These sources can be of different kinds--- for example, syntagmatic, paradigmatic or statistical information. Our approach combines various sources of knowledge, through combinations of the two WSD methods mentioned above. Mainly, the paper concentrates on how to combine these methods and sources of information in order to achieve good results in the disambiguation. Finally, this paper presents a comprehensive study and experimental work on evaluation of the methods and their combinations

SenseDefs : a multilingual corpus of semantically annotated textual definitions

Definitional knowledge has proved to be essential in various Natural Language Processing tasks and applications, especially when information at the level of word senses is exploited. However, the few sense-annotated corpora of textual definitions available to date are of limited size: this is mainly due to the expensive and time-consuming process of annotating a wide variety of word senses and entity mentions at a reasonably high scale. In this paper we present SenseDefs, a large-scale high-quality corpus of disambiguated definitions (or glosses) in multiple languages, comprising sense annotations of both concepts and named entities from a wide-coverage unified sense inventory. Our approach for the construction and disambiguation of this corpus builds upon the structure of a large multilingual semantic network and a state-of-the-art disambiguation system: first, we gather complementary information of equivalent definitions across different languages to provide context for disambiguation; then we refine the disambiguation output with a distributional approach based on semantic similarity. As a result, we obtain a multilingual corpus of textual definitions featuring over 38 million definitions in 263 languages, and we publicly release it to the research community. We assess the quality of SenseDefs’s sense annotations both intrinsically and extrinsically on Open Information Extraction and Sense Clustering tasks.Peer reviewe