Incorporating Speech Recognition Confidence into Discriminative Named Entity Recognition of Speech Data

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 617–624, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Incorporating speech recognition confidence into discriminative named entity recognition of speech data Katsuhito Sudoh Hajime Tsukada Hideki Isozaki NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation 2-4 Hikaridai, Seika-cho, Keihanna Science City, Kyoto 619-0237, Japan {sudoh,tsukada,isozaki}@cslab.kecl.ntt.co.jp Abstract This paper proposes a named entity recog- nition (NER) method for speech recogni- tion results that uses confidence on auto- matic speech recognition (ASR) as a fea- ture. The ASR confidence feature indi- cates whether each word has been cor- rectly recognized. The NER model is trained using ASR results with named en- tity (NE) labels as well as the correspond- ing transcriptions with NE labels. In ex- periments using support vector machines (SVMs) and speech data from Japanese newspaper articles, the proposed method outperformed a simple application of text- based NER to ASR results in NER F- measure by improving precision. These results show that the proposed method is effective in NER for noisy inputs. 1 Introduction As network bandwidths and storage capacities continue to grow, a large volume of speech data including broadcast news and PodCasts is becom- ing available. These data are important informa- tion sources as well as such text data as newspaper articles and WWW pages. Speech data as infor- mation sources are attracting a great deal of inter- est, such as DARPA’s global autonomous language exploitation (GALE) program. We also aim to use them for information extraction (IE), question an- swering, and indexing. Named entity recognition (NER) is a key tech- nique for IE and other natural language process- ing tasks. Named entities (NEs) are the proper ex- pressions for things such as peoples’ names, loca- tions’ names, and dates, and NER identifies those expressions and their categories. Unlike text data, speech data introduce automatic speech recogni- tion (ASR) error problems to NER. Although im- provements to ASR are needed, developing a ro- bust NER for noisy word sequences is also impor- tant. In this paper, we focus on the NER of ASR results and discuss the suppression of ASR error problems in NER. Most previous studies of the NER of speech data used generative models such as hidden Markov models (HMMs) (Miller et al., 1999; Palmer and Ostendorf, 2001; Horlock and King, 2003b; B´echet et al., 2004; Favre et al., 2005). On the other hand, in text-based NER, better re- sults are obtained using discriminative schemes such as maximum entropy (ME) models (Borth- wick, 1999; Chieu and Ng, 2003), support vec- tor machines (SVMs) (Isozaki and Kazawa, 2002), and conditional random fields (CRFs) (McCal- lum and Li, 2003). Zhai et al. (2004) applied a text-level ME-based NER to ASR results. These models have an advantage in utilizing various fea- tures, such as part-of-speech information, charac- ter types, and surrounding words, which may be overlapped, while overlapping features are hard to use in HMM-based models. To deal with ASR error …

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