38,718 research outputs found
Word graphs: The third set
This is the third paper in a series of natural language processing in term of knowledge graphs. A word is a basic unit in natural language processing. This is why we study word graphs. Word graphs were already built for prepositions and adwords (including adjectives, adverbs and Chinese quantity words) in two other papers. In this paper, we propose the concept of the logic word and classify logic words into groups in terms of semantics and the way they are used in describing reasoning processes. A start is made with the building of the lexicon of logic words in terms of knowledge graphs
Producing power-law distributions and damping word frequencies with two-stage language models
Standard statistical models of language fail to capture one of the most striking properties of natural languages: the power-law distribution in the frequencies of word tokens. We present a framework for developing statisticalmodels that can generically produce power laws, breaking generativemodels into two stages. The first stage, the generator, can be any standard probabilistic model, while the second stage, the adaptor, transforms the word frequencies of this model to provide a closer match to natural language. We show that two commonly used Bayesian models, the Dirichlet-multinomial model and the Dirichlet process, can be viewed as special cases of our framework. We discuss two stochastic processes-the Chinese restaurant process and its two-parameter generalization based on the Pitman-Yor process-that can be used as adaptors in our framework to produce power-law distributions over word frequencies. We show that these adaptors justify common estimation procedures based on logarithmic or inverse-power transformations of empirical frequencies. In addition, taking the Pitman-Yor Chinese restaurant process as an adaptor justifies the appearance of type frequencies in formal analyses of natural language and improves the performance of a model for unsupervised learning of morphology.48 page(s
An Essentialist Theory of the Meaning of Slurs
In this paper, I develop an essentialist model of the semantics of slurs. I defend the view that slurs are a species of kind terms: Slur concepts encode mini-theories which represent an essence-like element that is causally connected to a set of negatively-valenced stereotypical features of a social group. The truth-conditional contribution of slur nouns can then be captured by the following schema: For a given slur S of a social group G and a person P, S is true of P iff P bears the “essence” of G—whatever this essence is—which is causally responsible for stereotypical negative features associated with G and predicted of P. Since there is no essence that is causally responsible for stereotypical negative features of a social group, slurs have null-extension, and consequently, many sentences containing them are either meaningless or false. After giving a detailed outline of my theory, I show that it receives strong linguistic support. In particular, it can account for a wide range of linguistic cases that are regarded as challenging, central data for any theory of slurs. Finally, I show that my theory also receives convergent support from cognitive psychology and psycholinguistics
Dependency parsing of Turkish
The suitability of different parsing methods for different languages is an important topic in
syntactic parsing. Especially lesser-studied languages, typologically different from the languages
for which methods have originally been developed, poses interesting challenges in this respect.
This article presents an investigation of data-driven dependency parsing of Turkish, an agglutinative
free constituent order language that can be seen as the representative of a wider class
of languages of similar type. Our investigations show that morphological structure plays an
essential role in finding syntactic relations in such a language. In particular, we show that
employing sublexical representations called inflectional groups, rather than word forms, as the
basic parsing units improves parsing accuracy. We compare two different parsing methods, one
based on a probabilistic model with beam search, the other based on discriminative classifiers and
a deterministic parsing strategy, and show that the usefulness of sublexical units holds regardless
of parsing method.We examine the impact of morphological and lexical information in detail and
show that, properly used, this kind of information can improve parsing accuracy substantially.
Applying the techniques presented in this article, we achieve the highest reported accuracy for
parsing the Turkish Treebank
Chinese Function Tag Labeling
PACLIC 23 / City University of Hong Kong / 3-5 December 200
Modelling the Lexicon in Unsupervised Part of Speech Induction
Automatically inducing the syntactic part-of-speech categories for words in
text is a fundamental task in Computational Linguistics. While the performance
of unsupervised tagging models has been slowly improving, current
state-of-the-art systems make the obviously incorrect assumption that all
tokens of a given word type must share a single part-of-speech tag. This
one-tag-per-type heuristic counters the tendency of Hidden Markov Model based
taggers to over generate tags for a given word type. However, it is clearly
incompatible with basic syntactic theory. In this paper we extend a
state-of-the-art Pitman-Yor Hidden Markov Model tagger with an explicit model
of the lexicon. In doing so we are able to incorporate a soft bias towards
inducing few tags per type. We develop a particle filter for drawing samples
from the posterior of our model and present empirical results that show that
our model is competitive with and faster than the state-of-the-art without
making any unrealistic restrictions.Comment: To be presented at the 14th Conference of the European Chapter of the
Association for Computational Linguistic
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