34,390 research outputs found
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Improving Statistical Language Model Performance with Automatically Generated Word Hierarchies
An automatic word classification system has been designed which processes
word unigram and bigram frequency statistics extracted from a corpus of natural
language utterances. The system implements a binary top-down form of word
clustering which employs an average class mutual information metric. Resulting
classifications are hierarchical, allowing variable class granularity. Words
are represented as structural tags --- unique -bit numbers the most
significant bit-patterns of which incorporate class information. Access to a
structural tag immediately provides access to all classification levels for the
corresponding word. The classification system has successfully revealed some of
the structure of English, from the phonemic to the semantic level. The system
has been compared --- directly and indirectly --- with other recent word
classification systems. Class based interpolated language models have been
constructed to exploit the extra information supplied by the classifications
and some experiments have shown that the new models improve model performance.Comment: 17 Page Paper. Self-extracting PostScript Fil
The "handedness" of language: Directional symmetry breaking of sign usage in words
Language, which allows complex ideas to be communicated through symbolic
sequences, is a characteristic feature of our species and manifested in a
multitude of forms. Using large written corpora for many different languages
and scripts, we show that the occurrence probability distributions of signs at
the left and right ends of words have a distinct heterogeneous nature.
Characterizing this asymmetry using quantitative inequality measures, viz.
information entropy and the Gini index, we show that the beginning of a word is
less restrictive in sign usage than the end. This property is not simply
attributable to the use of common affixes as it is seen even when only word
roots are considered. We use the existence of this asymmetry to infer the
direction of writing in undeciphered inscriptions that agrees with the
archaeological evidence. Unlike traditional investigations of phonotactic
constraints which focus on language-specific patterns, our study reveals a
property valid across languages and writing systems. As both language and
writing are unique aspects of our species, this universal signature may reflect
an innate feature of the human cognitive phenomenon.Comment: 10 pages, 4 figures + Supplementary Information (15 pages, 8
figures), final corrected versio
Sentiment Analysis using an ensemble of Feature Selection Algorithms
To determine the opinion of any person experiencing any services or buying any product, the usage of Sentiment Analysis, a continuous research in the field of text mining, is a common practice. It is a process of using computation to identify and categorize opinions expressed in a piece of text. Individuals post their opinion via reviews, tweets, comments or discussions which is our unstructured information. Sentiment analysis gives a general conclusion of audits which benefit clients, individuals or organizations for decision making. The primary point of this paper is to perform an ensemble approach on feature reduction methods identified with natural language processing and performing the analysis based on the results. An ensemble approach is a process of combining two or more methodologies. The feature reduction methods used are Principal Component Analysis (PCA) for feature extraction and Pearson Chi squared statistical test for feature selection. The fundamental commitment of this paper is to experiment whether combined use of cautious feature determination and existing classification methodologies can yield better accuracy
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