2 research outputs found
Named entity extraction for speech
Named entity extraction is a field that has generated much interest over recent years
with the explosion of the World Wide Web and the necessity for accurate information
retrieval. Named entity extraction, the task of finding specific entities within documents,
has proven of great benefit for numerous information extraction and information retrieval
tasks.As well as multiple language evaluations, named entity extraction has been investigated
on a variety of media forms with varying success. In general, these media forms
have all been based upon standard text and assumed that any variation from standard
text constitutes noise.We investigate how it is possible to find named entities in speech data.. Where
others have focussed on applying named entity extraction techniques to transcriptions
of speech, we investigate a method for finding the named entities direct from the word
lattices associated with the speech signal. The results show that it is possible to improve
named entity recognition at the expense of word error rate (WER) in contrast to the
general view that F -score is directly proportional to WER.We use a. Hidden Markov Model {HMM) style approach to the task of named entity
extraction and show how it is possible to utilise a HMM to find named entities
within speech lattices. We further investigate how it is possible to improve results by
considering an alternative derivation of the joint probability of words and entities than
is traditionally used. This new derivation is particularly appropriate to speech lattices
as no presumptions are made about the sequence of words.The HMM style approach that we use requires using a number of language models
in parallel. We have developed a system for discriminately retraining these language
models based upon the results of the output, and we show how it is possible to improve
named entity recognition by iterations over both training data and development data.
We also consider how part-of-speech (POS) can be used within word lattices. We
devise a method of labelling a word lattice with POS tags and adapt the model to make
use of these POS tags when producing the best path through the lattice. The resulting
path provides the most likely sequence of words, entities and POS tags and we show
how this new path is better than the previous path which ignored the POS tags
Named entity tagged language models.
We introduce Named Entity (NE) Language Modelling, a stochastic finite state machine approach to identifying both words and NE categories from a stream of spoken data. We provide an overview of our approach to NE tagged language model (LM) generation together with results of the application of such a LM to the task of out-of-vocabulary (OOV) word reduction in large vocabulary speech recognition. Using the Wall Street Journal and Broadcast News corpora, it is shown that the tagged LM was able to reduce the overall word error rate by 14%, detecting up to 70% of previously OOV words. We also describe an example of the direct tagging of spoken data with NE categories