this study focuses on one particular sound change common in conversational speech, in which coronal nasal consonants undergo place assimilation toward the following syllable- or word-initial labial consonants. f2 and f3 formant frequency measurements were taken from, and compared between words involving the sequences /vnb/ and /vmb/ across syllable boundary as well as word boundary in assimilat...

The goal of the Cross-lingual Word Sense Disambiguation task is to evaluate the viability of multilingual WSD on a benchmark lexical sample data set. The traditional WSD task is transformed into a multilingual WSD task, where participants are asked to provide contextually correct translations of English ambiguous nouns into five target languages, viz. French, Italian, English, German and Dutch....

In modern day scenario, it is observed that the content available on Internet is in very large amount. Any single search will result in millions of results, thus the user faces the issues like which content to choose and which one to not. One of the classification in data mining is content hierarchy, which can be defined as the extraction of content in logical and related manner. The key focus ...

This paper explores the automatic learning of distributed representations of the target’s context for semantic frame labeling with target-based neural model. We constrain the whole sentence as the model’s input without feature extraction from the sentence. This is different from many previous works in which local feature extraction of the targets is widely used. This constraint makes the task h...

Mainstream word sense disambiguation systems have relied mostly on supervised approaches. Complex interactions have been observed between learning algorithms and knowledge sources, but the factors underlying such phenomena are underexplored. This calls for more qualitative analysis of disambiguation results, possibly from an inter-disciplinary perspective. The current study thus preliminarily e...

We propose a multilingual unsupervised Word Sense Disambiguation (WSD) task for a sample of English nouns. Instead of providing manually sensetagged examples for each sense of a polysemous noun, our sense inventory is built up on the basis of the Europarl parallel corpus. The multilingual setup involves the translations of a given English polysemous noun in five supported languages, viz. Dutch,...

This article introduces an unsupervised word sense disambiguation algorithm that is inspired by the lexical attraction models of Yuret (1998). It is based on the assumption that the meanings of the words that form a sentence can be best assigned by constructing an interpretation of the whole sentence. This interpretation is facilitated by a dependency-like context specification of a content wor...

Word-sense recognition and disambiguation (WERD) is the task of identifying word phrases and their senses in natural language text. Though it is well understood how to disambiguate noun phrases, this task is much less studied for verbs and verbal phrases. We present Werdy, a framework for WERD with particular focus on verbs and verbal phrases. Our framework first identifies multi-word expressio...

Evaluating a WSD task is a challenge at least as difficult as developing the task itself. Manually constructing a corpus of ambiguous sentences is a difficult and tedious task. Such a corpus is even more complex to produce if in each sentence, each ambiguous word is associated with its correct meaning. In addition to finding/imagining the sentence, it is necessary to have a word sense lexicon t...