Amruta Purandare

Name discrimination by clustering similar contexts

It is relatively common for different people or organizations to share the same name. Given the increasing amount of information available online, this results in the ever growing possibility of finding misleading or incorrect information due to confusion caused by an ambiguous name. This paper presents an unsupervised approach that resolves name ambiguity by clustering the instances of a given name into groups, each of which is associated with a distinct underlying entity. The features we employ to represent the context of an ambiguous name are statistically significant bigrams that occur in the same context as the ambiguous name. From these features we create a co–occurrence matrix where the rows and columns represent the first and second words in bigrams, and the cells contain their log–likelihood scores. Then we represent each of the contexts in which an ambiguous name appears with a second order context vector. This is created by taking the average of the vectors from the co–occurrence matrix associated with the words that make up each context. This creates a high dimensional “instance by word” matrix that is reduced to its most significant dimensions by Singular Value Decomposition (SVD). The different “meanings” of a name are discriminated by clustering these second order context vectors with the method of Repeated Bisections. We evaluate this approach by conflating pairs of names found in a large corpus of text to create ambiguous pseudo-names. We find that our method is significantly more accurate than the majority classifier, and that the best results are obtained by having a small amount of local context to represent the instance, along with a larger amount of context for identifying features, or vice versa.

Humor: Prosody Analysis and Automatic Recognition for F*R*I*E*N*D*S*

We analyze humorous spoken conversations from a classic comedy television show, FRIENDS, by examining acoustic-prosodic and linguistic features and their utility in automatic humor recognition. Using a simple annotation scheme, we automatically label speaker turns in our corpus that are followed by laughs as humorous and the rest as non-humorous. Our humor-prosody analysis reveals significant differences in prosodic characteristics (such as pitch, tempo, energy etc.) of humorous and non-humorous speech, even when accounted for the gender and speaker differences. Humor recognition was carried out using standard supervised learning classifiers, and shows promising results significantly above the baseline.

Discriminating among word senses using McQuitty's similarity analysis

This paper presents an unsupervised method for discriminating among the senses of a given target word based on the context in which it occurs. Instances of a word that occur in similar contexts are grouped together via McQuittys Similarity Analysis, an agglomerative clustering algorithm. The context in which a target word occurs is represented by surface lexical features such as unigrams, bigrams, and second order co-occurrences. This paper summarizes our approach, and describes the results of a preliminary evaluation we have carried out using data from the SENSEVAL-2 English lexical sample and the line corpus.