17,888 research outputs found
Syntactic Topic Models
The syntactic topic model (STM) is a Bayesian nonparametric model of language
that discovers latent distributions of words (topics) that are both
semantically and syntactically coherent. The STM models dependency parsed
corpora where sentences are grouped into documents. It assumes that each word
is drawn from a latent topic chosen by combining document-level features and
the local syntactic context. Each document has a distribution over latent
topics, as in topic models, which provides the semantic consistency. Each
element in the dependency parse tree also has a distribution over the topics of
its children, as in latent-state syntax models, which provides the syntactic
consistency. These distributions are convolved so that the topic of each word
is likely under both its document and syntactic context. We derive a fast
posterior inference algorithm based on variational methods. We report
qualitative and quantitative studies on both synthetic data and hand-parsed
documents. We show that the STM is a more predictive model of language than
current models based only on syntax or only on topics
Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature
Over the past 50 years many have debated what representation should be used
to capture the meaning of natural language utterances. Recently new needs of
such representations have been raised in research. Here I survey some of the
interesting representations suggested to answer for these new needs.Comment: 15 pages, no figure
A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation
In this paper, we introduce a trie-structured Bayesian model for unsupervised
morphological segmentation. We adopt prior information from different sources
in the model. We use neural word embeddings to discover words that are
morphologically derived from each other and thereby that are semantically
similar. We use letter successor variety counts obtained from tries that are
built by neural word embeddings. Our results show that using different
information sources such as neural word embeddings and letter successor variety
as prior information improves morphological segmentation in a Bayesian model.
Our model outperforms other unsupervised morphological segmentation models on
Turkish and gives promising results on English and German for scarce resources.Comment: 12 pages, accepted and presented at the CICLING 2017 - 18th
International Conference on Intelligent Text Processing and Computational
Linguistic
On the accuracy of language trees
Historical linguistics aims at inferring the most likely language
phylogenetic tree starting from information concerning the evolutionary
relatedness of languages. The available information are typically lists of
homologous (lexical, phonological, syntactic) features or characters for many
different languages.
From this perspective the reconstruction of language trees is an example of
inverse problems: starting from present, incomplete and often noisy,
information, one aims at inferring the most likely past evolutionary history. A
fundamental issue in inverse problems is the evaluation of the inference made.
A standard way of dealing with this question is to generate data with
artificial models in order to have full access to the evolutionary process one
is going to infer. This procedure presents an intrinsic limitation: when
dealing with real data sets, one typically does not know which model of
evolution is the most suitable for them. A possible way out is to compare
algorithmic inference with expert classifications. This is the point of view we
take here by conducting a thorough survey of the accuracy of reconstruction
methods as compared with the Ethnologue expert classifications. We focus in
particular on state-of-the-art distance-based methods for phylogeny
reconstruction using worldwide linguistic databases.
In order to assess the accuracy of the inferred trees we introduce and
characterize two generalizations of standard definitions of distances between
trees. Based on these scores we quantify the relative performances of the
distance-based algorithms considered. Further we quantify how the completeness
and the coverage of the available databases affect the accuracy of the
reconstruction. Finally we draw some conclusions about where the accuracy of
the reconstructions in historical linguistics stands and about the leading
directions to improve it.Comment: 36 pages, 14 figure
Induction of Word and Phrase Alignments for Automatic Document Summarization
Current research in automatic single document summarization is dominated by
two effective, yet naive approaches: summarization by sentence extraction, and
headline generation via bag-of-words models. While successful in some tasks,
neither of these models is able to adequately capture the large set of
linguistic devices utilized by humans when they produce summaries. One possible
explanation for the widespread use of these models is that good techniques have
been developed to extract appropriate training data for them from existing
document/abstract and document/headline corpora. We believe that future
progress in automatic summarization will be driven both by the development of
more sophisticated, linguistically informed models, as well as a more effective
leveraging of document/abstract corpora. In order to open the doors to
simultaneously achieving both of these goals, we have developed techniques for
automatically producing word-to-word and phrase-to-phrase alignments between
documents and their human-written abstracts. These alignments make explicit the
correspondences that exist in such document/abstract pairs, and create a
potentially rich data source from which complex summarization algorithms may
learn. This paper describes experiments we have carried out to analyze the
ability of humans to perform such alignments, and based on these analyses, we
describe experiments for creating them automatically. Our model for the
alignment task is based on an extension of the standard hidden Markov model,
and learns to create alignments in a completely unsupervised fashion. We
describe our model in detail and present experimental results that show that
our model is able to learn to reliably identify word- and phrase-level
alignments in a corpus of pairs
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