49,267 research outputs found
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
Acquiring Correct Knowledge for Natural Language Generation
Natural language generation (NLG) systems are computer software systems that
produce texts in English and other human languages, often from non-linguistic
input data. NLG systems, like most AI systems, need substantial amounts of
knowledge. However, our experience in two NLG projects suggests that it is
difficult to acquire correct knowledge for NLG systems; indeed, every knowledge
acquisition (KA) technique we tried had significant problems. In general terms,
these problems were due to the complexity, novelty, and poorly understood
nature of the tasks our systems attempted, and were worsened by the fact that
people write so differently. This meant in particular that corpus-based KA
approaches suffered because it was impossible to assemble a sizable corpus of
high-quality consistent manually written texts in our domains; and structured
expert-oriented KA techniques suffered because experts disagreed and because we
could not get enough information about special and unusual cases to build
robust systems. We believe that such problems are likely to affect many other
NLG systems as well. In the long term, we hope that new KA techniques may
emerge to help NLG system builders. In the shorter term, we believe that
understanding how individual KA techniques can fail, and using a mixture of
different KA techniques with different strengths and weaknesses, can help
developers acquire NLG knowledge that is mostly correct
Analyzing analytical methods: The case of phonology in neural models of spoken language
Given the fast development of analysis techniques for NLP and speech
processing systems, few systematic studies have been conducted to compare the
strengths and weaknesses of each method. As a step in this direction we study
the case of representations of phonology in neural network models of spoken
language. We use two commonly applied analytical techniques, diagnostic
classifiers and representational similarity analysis, to quantify to what
extent neural activation patterns encode phonemes and phoneme sequences. We
manipulate two factors that can affect the outcome of analysis. First, we
investigate the role of learning by comparing neural activations extracted from
trained versus randomly-initialized models. Second, we examine the temporal
scope of the activations by probing both local activations corresponding to a
few milliseconds of the speech signal, and global activations pooled over the
whole utterance. We conclude that reporting analysis results with randomly
initialized models is crucial, and that global-scope methods tend to yield more
consistent results and we recommend their use as a complement to local-scope
diagnostic methods.Comment: ACL 202
Persuading developers to buy into software process improvement: an exploratory analysis
In order to investigate practitioners' opinions of software process and software process improvement, we have collected information from 13 companies, in a variety of ways i.e. the use of Repertory Grid Technique, survey and focus group discussions. Both the Repertory Grid Technique and the focus group discussions (43 discussions occurred, in total) produced a large volume of qualitative data. At the same time, other researchers have reported--investigations of practitioners, and we are interested in how their reports may relate to our own. Thus, other research publications can also be treated as a form of qualitative data. In this paper, we review advice on a method, content analysis, that is used to analyse qualitative data. Content analysis is a method for identifying and classifying words and phrases used in--ordinary language. We use content analysis to describe and analyse discussions on software--process and software process improvement. We report preliminary findings from an analysis--of both the focus group evidence and some publications. Our main finding is that there is an--apparent contradiction between developers saying that they want evidence for software process improvement, and what developers will accept as evidence. This presents a serious problem for research: even if researchers could demonstrate a strong, reliable relationship between software process improvement and improved organisational performance, there would still be the problem of convincing practitioners that the evidence applies to their particular situation
Comparative Analysis of Word Embeddings for Capturing Word Similarities
Distributed language representation has become the most widely used technique
for language representation in various natural language processing tasks. Most
of the natural language processing models that are based on deep learning
techniques use already pre-trained distributed word representations, commonly
called word embeddings. Determining the most qualitative word embeddings is of
crucial importance for such models. However, selecting the appropriate word
embeddings is a perplexing task since the projected embedding space is not
intuitive to humans. In this paper, we explore different approaches for
creating distributed word representations. We perform an intrinsic evaluation
of several state-of-the-art word embedding methods. Their performance on
capturing word similarities is analysed with existing benchmark datasets for
word pairs similarities. The research in this paper conducts a correlation
analysis between ground truth word similarities and similarities obtained by
different word embedding methods.Comment: Part of the 6th International Conference on Natural Language
Processing (NATP 2020
Persuading developers to buy into software process improvement: a local opinion and empirical evidence
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.---- Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.In order to investigate practitionersâ opinions of software process and software process improvement, we have collected a large volume of qualitative evidence from 13 companies. At the same time, other researchers have reported investigations of practitioners, and we are interested in how their reports may relate to our evidence. Thus, other research publications can also be treated as a form of qualitative data. In this paper, we review advice on a method, content analysis, that is used to analyse qualitative data. We use content analysis to describe and analyse discussions on software process and software process improvement. We report preliminary findings from an analysis of both the focus group evidence and four publications
Analyzing and Improving Statistical Language Models for Speech Recognition
In many current speech recognizers, a statistical language model is used to
indicate how likely it is that a certain word will be spoken next, given the
words recognized so far. How can statistical language models be improved so
that more complex speech recognition tasks can be tackled? Since the knowledge
of the weaknesses of any theory often makes improving the theory easier, the
central idea of this thesis is to analyze the weaknesses of existing
statistical language models in order to subsequently improve them. To that end,
we formally define a weakness of a statistical language model in terms of the
logarithm of the total probability, LTP, a term closely related to the standard
perplexity measure used to evaluate statistical language models. We apply our
definition of a weakness to a frequently used statistical language model,
called a bi-pos model. This results, for example, in a new modeling of unknown
words which improves the performance of the model by 14% to 21%. Moreover, one
of the identified weaknesses has prompted the development of our generalized
N-pos language model, which is also outlined in this thesis. It can incorporate
linguistic knowledge even if it extends over many words and this is not
feasible in a traditional N-pos model. This leads to a discussion of
whatknowledge should be added to statistical language models in general and we
give criteria for selecting potentially useful knowledge. These results show
the usefulness of both our definition of a weakness and of performing an
analysis of weaknesses of statistical language models in general.Comment: 140 pages, postscript, approx 500KB, if problems with delivery, mail
to [email protected]
Software Infrastructure for Natural Language Processing
We classify and review current approaches to software infrastructure for
research, development and delivery of NLP systems. The task is motivated by a
discussion of current trends in the field of NLP and Language Engineering. We
describe a system called GATE (a General Architecture for Text Engineering)
that provides a software infrastructure on top of which heterogeneous NLP
processing modules may be evaluated and refined individually, or may be
combined into larger application systems. GATE aims to support both researchers
and developers working on component technologies (e.g. parsing, tagging,
morphological analysis) and those working on developing end-user applications
(e.g. information extraction, text summarisation, document generation, machine
translation, and second language learning). GATE promotes reuse of component
technology, permits specialisation and collaboration in large-scale projects,
and allows for the comparison and evaluation of alternative technologies. The
first release of GATE is now available - see
http://www.dcs.shef.ac.uk/research/groups/nlp/gate/Comment: LaTeX, uses aclap.sty, 8 page
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