Inducing Features of Random Fields

We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The statistical modeling techniques introduced in this paper differ from those common to much of the natural language processing literature since there is no probabilistic finite state or push-down automaton on which the model is built. Our approach also differs from the techniques common to the computer vision literature in that the underlying random fields are non-Markovian and have a large number of parameters that must be estimated. Relations to other learning approaches including decision trees and Boltzmann machines are given. As a demonstration of the method, we describe its application to the problem of automatic word classification in natural language processing. Key words: random field, Kullback-Leibler divergence, iterative scaling, divergence geometry, maximum entropy, EM algorithm, statistical learning, clustering, word morphology, natural language processingComment: 34 pages, compressed postscrip

Neural models for unsupervised disambiguation in morphologically rich languages

The problem of morphological ambiguity is central to many natural language processing tasks. In particular, morphologically rich languages pose a unique challenge due to the large number of possible forms some words can take. In this work, we implement and evaluate a method for morphological disambiguation of morphologically rich languages. We use deep learning techniques to build a disambiguation model and leverage existing tools to automatically generate a training data set. We evaluate our approach on the Finnish, Russian and Spanish languages. For these languages, our method surpasses the state-of-the-art results for the tasks of part-of-speech and lemma disambiguation

Mapping text to knowledge graph entities using multi-sense LSTMs

This paper addresses the problem of mapping natural language text to knowledge base entities. The mapping process is approached as a composition of a phrase or a sentence into a point in a multi-dimensional entity space obtained from a knowledge graph. The compositional model is an LSTM equipped with a dynamic disambiguation mechanism on the input word embeddings (a Multi-Sense LSTM), addressing polysemy issues. Further, the knowledge base space is prepared by collecting random walks from a graph enhanced with textual features, which act as a set of semantic bridges between text and knowledge base entities. The ideas of this work are demonstrated on large-scale text-to-entity mapping and entity classification tasks, with state of the art results.NVidia Corporation for the donation of a Titan XP GP