NLP Problems and Their Solutions

Multiword expressions have been a challenge for NLP applications. For instance, phrasal verbs have been difficult for many NLP applications to decode. Pichotta and DeNero (2013) who focusing on English phrasal verbs, used a novel ranking-oriented boosting algorithm on many parallel corpora to produce a set of English phrasal verbs provided improvement in this aspect. The output was of a quality comparable to a human-curated set. Integration of this algorithm into the applications of NLP leads to increased accuracy in detection and interpretation of phrasal verbs.

If this sample essay on"NLP Problems and Their Solutions" doesn’t help,
our writers will!

Trust an expert

Mesnil et al., (2015) provided a solution to the problem of semantic slot filling in spoken language understanding (SLU). From their research, Mesnil et a., provided a solution based on recurrent neural networks (RNNs) to handle semantic slot filling. In addition, they provided novel architectures whose structure was developed to perfect past and future temporal dependencies by increasing their efficiency. Several significant RNN architectures were executed and compared before juxtaposing with conditional random field (CRF) baseline. The RNN were established to be more efficient in general use, movies and entertainment.

Background noise and far-field conditions make it difficult for NLP applications to effectively carry out tasks especially keyword spotting. However, the use of deep neural networks (DNNs) has been established to increase effectiveness of applications in keyword spotting (Prabhavalkar et al., 2015). Using automatic gain control (AGC), the level of background noise and speech are estimated with high accuracy. Together with multi-style training, it is possible to significantly improve system performance (Prabhavalkar et al., 2015).

Natural language processing is the creation of a link that enables computers to communicate and understand human languages. The idea of natural language processing has been in existence for as long as the idea of computers has existed. The Second World War period was significant in the progression of NLP technology. The past five years have been instrumental in the advancement of NLP technology. Research has established several advances such as increased accuracy in decoding for syntactic parsing that have increased the efficiency of NLP applications.

References

Bodenstab, N., Dunlop, A., Hall, K., & Roark, B. (2011, June). Beam-width prediction for efficient context-free parsing. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1 (pp. 440-449). Association for Computational Linguistics.Chowdhury, G. G. (2003). Natural language processing. Annual review of information science and technology, 37(1), 51-89.

Filippova, K., & Altun, Y. (2013). Overcoming the Lack of Parallel Data in Sentence

Compression. In EMNLP (pp. 1481-1491).Jones, K. S. (1994). Natural language processing: a historical review. InCurrent issues in

computational linguistics: in honour of Don Walker (pp. 3-16). Springer Netherlands.Jurafsky, D., & Martin, J. H. (2009). Speech and language processing: An introduction to

natural language processing, computational linguistics, and speech recognition. Upper Saddle River, NJ: Pearson Prentice Hall.Jyothi, P., Johnson, L., Chelba, C., & Strope, B. (2012, June). Large-scale discriminative

language model reranking for voice-search. In Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT (pp. 41-49). Association for Computational Linguistics.Mesnil, G., Dauphin, Y., Yao, K., Bengio, Y., Deng, L., Hakkani-Tur, D., ... & Zweig, G.

(2015). Using recurrent neural networks for slot filling in spoken language understanding. Audio, Speech, and Language Processing, IEEE/ACM Transactions on, 23(3), 530-539.Pichotta, K., & DeNero, J. (2013). Identifying Phrasal Verbs Using Many Bilingual Corpora.

In EMNLP (pp. 636-646).Prabhavalkar, R., Alvarez, R., Parada, C., Nakkiran, P., & Sainath, T. N. (2015, April).

Automatic gain control and multi-style training for robust small-footprint keyword spotting with deep neural networks. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on(pp. 4704-4708). IEEE.