End-to-end named entity recognition for spoken Finnish

Named entity recognition is a natural language processing task in which the system tries to find named entities and classify them in predefined categories. The categories can vary, depending on the domain in which they are going to be used but some of the most common include: person, location, organization, date and product. Named entity recognition is an integral part of other large natural language processing tasks, such as information retrieval, text summarization, machine translation, and question answering. Doing named entity recognition is a difficult task due to the lack of annotated data for certain languages or domains. Named entity ambiguity is another challenging aspect that arises when doing named entity recognition. Often times, a word can represent a person, organization, product, or any other category, depending on the context it appears in. Spoken data, which can be the output of a speech recognition system, imposes additional challenges to the named entity recognition system. Named entities are often capitalized and the system learns to rely on capitalization in order to detect the entities, which is neglected in the speech recognition output. The standard way of doing named entity recognition from speech involves a pipeline approach of two systems. First, a speech recognition system transcribes the speech and generates the transcripts, after which a named entity recognition system annotates the transcripts with the named entities. Since the speech recognition system is not perfect and makes errors, those errors are propagated to the named entity recognition system, which is hard to recover from. In this thesis, we present two approaches of doing named entity recognition from Finnish speech in an end-to-and manner, where one system generates the transcripts and the annotations. We will explore the strengths and weaknesses of both approaches and see how they compare to the standard pipeline approach

Do Multi-Sense Embeddings Improve Natural Language Understanding?

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained models of vector-space representations. Yet while `multi-sense' methods have been proposed and tested on artificial word-similarity tasks, we don't know if they improve real natural language understanding tasks. In this paper we introduce a multi-sense embedding model based on Chinese Restaurant Processes that achieves state of the art performance on matching human word similarity judgments, and propose a pipelined architecture for incorporating multi-sense embeddings into language understanding. We then test the performance of our model on part-of-speech tagging, named entity recognition, sentiment analysis, semantic relation identification and semantic relatedness, controlling for embedding dimensionality. We find that multi-sense embeddings do improve performance on some tasks (part-of-speech tagging, semantic relation identification, semantic relatedness) but not on others (named entity recognition, various forms of sentiment analysis). We discuss how these differences may be caused by the different role of word sense information in each of the tasks. The results highlight the importance of testing embedding models in real applications

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

A Proof-of-Concept for Orthographic Named Entity Correction in Spanish Voice Queries

Proceedings of: 10th International Workshop on Adaptive Multimedia Retrieval. Took place October 24-25, 2012, in Copenhaguen (Denmark).Automatic speech recognition (ASR) systems are not able to recognize entities that are not present in its vocabulary. The problem considered in this paper is the misrecognition of named entities in Spanish voice queries introducing a proof-of-concept for named entity correction that provides alternative entities to the ones incorrectly recognized or misrecognized by retrieving entities phonetically similar from a dictionary. This system is domain-dependent, using sports news, especially football news, regardless of the automatic speech recognition system used. The correction process exploits the query structure and its semantic information to detect where a named entity appears. The system finds the most suitable alternative entity from a dictionary previously generated with the existing named entities.This work has been partially supported by the Regional Government of Madrid under the Research Network MA2VICMR (S2009/TIC-1542) and by the Spanish Center for Industry Technological Development (CDTI, Ministry of Industry, Tourism and Trade) through the BUSCAMEDIA Project (CEN-20091026).Publicad

Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer

In spite of the excellent strides made by end-to-end (E2E) models in speech recognition in recent years, named entity recognition is still challenging but critical for semantic understanding. In order to enhance the ability to recognize named entities in E2E models, previous studies mainly focus on various rule-based or attention-based contextual biasing algorithms. However, their performance might be sensitive to the biasing weight or degraded by excessive attention to the named entity list, along with a risk of false triggering. Inspired by the success of the class-based language model (LM) in named entity recognition in conventional hybrid systems and the effective decoupling of acoustic and linguistic information in the factorized neural Transducer (FNT), we propose a novel E2E model to incorporate class-based LMs into FNT, which is referred as C-FNT. In C-FNT, the language model score of named entities can be associated with the name class instead of its surface form. The experimental results show that our proposed C-FNT presents significant error reduction in named entities without hurting performance in general word recognition