887 research outputs found
On the Disambiguation of Weighted Automata
We present a disambiguation algorithm for weighted automata. The algorithm
admits two main stages: a pre-disambiguation stage followed by a transition
removal stage. We give a detailed description of the algorithm and the proof of
its correctness. The algorithm is not applicable to all weighted automata but
we prove sufficient conditions for its applicability in the case of the
tropical semiring by introducing the *weak twins property*. In particular, the
algorithm can be used with all acyclic weighted automata, relevant to
applications. While disambiguation can sometimes be achieved using
determinization, our disambiguation algorithm in some cases can return a result
that is exponentially smaller than any equivalent deterministic automaton. We
also present some empirical evidence of the space benefits of disambiguation
over determinization in speech recognition and machine translation
applications
Sampling from Stochastic Finite Automata with Applications to CTC Decoding
Stochastic finite automata arise naturally in many language and speech
processing tasks. They include stochastic acceptors, which represent certain
probability distributions over random strings. We consider the problem of
efficient sampling: drawing random string variates from the probability
distribution represented by stochastic automata and transformations of those.
We show that path-sampling is effective and can be efficient if the
epsilon-graph of a finite automaton is acyclic. We provide an algorithm that
ensures this by conflating epsilon-cycles within strongly connected components.
Sampling is also effective in the presence of non-injective transformations of
strings. We illustrate this in the context of decoding for Connectionist
Temporal Classification (CTC), where the predictive probabilities yield
auxiliary sequences which are transformed into shorter labeling strings. We can
sample efficiently from the transformed labeling distribution and use this in
two different strategies for finding the most probable CTC labeling
Use of Weighted Finite State Transducers in Part of Speech Tagging
This paper addresses issues in part of speech disambiguation using
finite-state transducers and presents two main contributions to the field. One
of them is the use of finite-state machines for part of speech tagging.
Linguistic and statistical information is represented in terms of weights on
transitions in weighted finite-state transducers. Another contribution is the
successful combination of techniques -- linguistic and statistical -- for word
disambiguation, compounded with the notion of word classes.Comment: uses psfig, ipamac
Probabilistic parsing
Postprin
Jumping Finite Automata for Tweet Comprehension
Every day, over one billion social media text messages are generated worldwide, which provides abundant information that can lead to improvements in lives of people through evidence-based decision making. Twitter is rich in such data but there are a number of technical challenges in comprehending tweets including ambiguity of the language used in tweets which is exacerbated in under resourced languages. This paper presents an approach based on Jumping Finite Automata for automatic comprehension of tweets. We construct a WordNet for the language of Kenya (WoLK) based on analysis of tweet structure, formalize the space of tweet variation and abstract the space on a Finite Automata. In addition, we present a software tool called Automata-Aided Tweet Comprehension (ATC) tool that takes raw tweets as input, preprocesses, recognise the syntax and extracts semantic information to 86% success rate
Memory-Based Learning: Using Similarity for Smoothing
This paper analyses the relation between the use of similarity in
Memory-Based Learning and the notion of backed-off smoothing in statistical
language modeling. We show that the two approaches are closely related, and we
argue that feature weighting methods in the Memory-Based paradigm can offer the
advantage of automatically specifying a suitable domain-specific hierarchy
between most specific and most general conditioning information without the
need for a large number of parameters. We report two applications of this
approach: PP-attachment and POS-tagging. Our method achieves state-of-the-art
performance in both domains, and allows the easy integration of diverse
information sources, such as rich lexical representations.Comment: 8 pages, uses aclap.sty, To appear in Proc. ACL/EACL 9
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