Seminar Series

Arabic Semantic Role Labeling Using Kernel Methods

There is a widely held belief in the natural language and computational linguistics communities that identifying and defining roles of predicate arguments in a sentence has a lot of potential for and Semantic Role Labeling (SRL) is a significant step toward improving important applications, e.g. question answering and information extraction. Despite SRL Systems have been largely studied for English, a long path has still to be done to design an satisfying system for Arabic. In this talk, I will present an SRL system for Modern Standard Arabic that exploits many aspects of the rich morphological features of the language. The experiments on the pilot Arabic Propbank data shows that our system based on Support Vector Machines and Kernel Methods yields a global SRL F1 score of 82.17, which improves the current state-of-the-art in Arabic SRL. In the process I will introduce features of the Arabic language that are relevant for automatic processing in general and to the task of SRL In particular. I will also describe the Arabic propbank highlighting how different it is than the English Propbank.

[Please note unusual day and time]

A Probabilistic Model of Word Usage Applied to the Lexical Substitution Task

The talk addresses the English Lexical Substitution Task: given a target word and the sentence in which it appears, the task is to find candidate word(s) which could be used in place of the target word without altering the sentence's meaning. I will present a method for performing this task, which incorporates primitive notions both of whether the candidate word matches the target word's semantics, and also whether the candidate word is grammatically coherent in the target word's place. Whilst approaches to this task thus far have largely concentrated on one or another of these concerns, the method we present shows that using a full probability model defined over random variables designed to represent both aspects of the sentence is both achievable and demonstrates significant improvement over either of the aspects alone. I suggest that, coupled with an optimal strategy for determining the set of candidate words, the technique would achieve state-of-the-art performance.

Sorting Out Dependency Parsing

The first part of the talk introduces the transition-based approach to data-driven dependency parsing, where inference is performed as a greedy best-first search over a non-deterministic transition system, while learning is reduced to the simple classification problem of mapping each parser state to the correct transition out of that state. The second part of the talk explores the idea that non-projective dependency parsing can be conceived as the outcome of two interleaved processes, one that sorts the words of a sentence into a canonical order, and one that performs strictly projective dependency parsing on the sorted input. Based on this idea, a parsing algorithm is constructed by combining an online sorting algorithm with a transition system for projective dependency parsing.

Fine-Grained Sentiment Analysis in Text and Multi-Party Conversation

The past several years have seen a huge growth in research on identifying and characterizing opinions and sentiments in text. While much of this work has focused on classifying the sentiment of documents, a more fine-grained analysis at the sentence level and below is needed for any application that seeks detailed opinion information, e.g., opinion question answering. In this talk, I will present work on fine-grained sentiment analysis in both text and multi-party conversation. The approaches taken are quite different in terms of the features explored. For text, a wide range of linguistically motivated features are employed for determining when instances of polarity terms are indeed being used to express positive and negative sentiments in context. The results of this disambiguation are then used in determining sentence polarity. For sentiment analysis in speech, the focus is on exploiting very shallow linguistic features, such as n-grams of characters and phonemes, for classifying the subjectivity and sentiment of utterances.

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