Clustering Algorithm

Our work focuses on identifying various types of lexical data in large corpora through statistical analysis. In this paper, we present a method for grouping adjectives according to their mean- …...

In this paper we present a method to group adjectives according to their meaning, as a first step towards the automatic identification of adjectival scales. We discuss the properties of …...

In statistical natural language process- ing we always face the problem of sparse data. One way to reduce this problem is to group words into equivalence classes which is a …...

This paper introduces a new, unsupervised algo- rithm for noun phrase coreference resolution. It dif- fers from existing methods in that it views corer- erence resolution as a clustering task. …...

We present a clustering algorithm for Arabic words sharing the same root. Root based clusters can substitute dictionaries indexing for IR. Modifying Adamson andin Boreham (1974), our Two-stage algorithm applies …...

We propose a novel method to predict the inter- paragraph discourse structure of text, i.e. to infer which paragraphs are related to each other and form larger segments on a …...

This paper discusses two problems that arise in the Generation of Referring Expressions: (a) numeric-valued attributes, such as size or location; (b) perspective-taking in reference. Both problems, it is argued, …...

Previous methods usually conduct the keyphrase extraction task for single docu- ments separately without interactions for each document, under the assumption that the documents are considered inde- pendent of each …...

We propose using large-scale clustering of de- pendency relations between verbs and multi- word nouns (MNs) to construct a gazetteer for named entity recognition (NER). Since depen- dency relations capture …...

. The development of the Web services lets many users easily provide their opinions recently. Automatic summarization of enormous sentiments has been expected. Intuitively, we can summarize a review with …...

In this paper we describe an unsupervised method for semantic role induction which holds promise for relieving the data acqui- sition bottleneck associated with supervised role labelers. We present an …...

Hidden properties of social media users, such as their ethnicity, gender, and location, are of- ten reflected in their observed attributes, such as their first and last names. Furthermore, users …...

AbstractThe paper investigates the relation between metaphoricity and distributional characteristics of verbs, introducing POM, a corpus-derived index that can be used to define the upper bound of metaphoricity of any …...

AbstractExisting approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual …...

AbstractWord meaning is notoriously difficult to capture, both synchronically and diachronically. In this paper, we describe the creation of the largest resource of graded contextualized, diachronic word meaning annotation in …...