Natural Language Processing: A Survey
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paper_title: Coping With Syntactic Ambiguity Or How To Put The Block In The Box On The Table

paper_content:

Sentences are far more ambiguous than one might have thought. There may be hundreds, perhaps thousands, of syntactic parse trees for certain very natural sentences of English. This fact has been a major problem confronting natural language processing, especially when a large percentage of the syntactic parse trees are enumerated during semantic/pragmatic processing. In this paper we propose some methods for dealing with syntactic ambiguity in ways that exploit certain regularities among alternative parse trees. These regularities will be expressed as linear combinations of ATN networks, and also as sums and products of formal power series. We believe that such encoding of ambiguity will enhance processing, whether syntactic and semantic constraints are processed separately in sequence or interleaved together.
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paper_title: An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery

paper_content:

This paper presents a model-based, unsupervised algorithm for recovering word boundaries in a natural-language text from which they have been deleted. The algorithm is derived from a probability model of the source that generated the text. The fundamental structure of the model is specified abstractly so that the detailed component models of phonology, word-order, and word frequency can be replaced in a modular fashion. The model yields a language-independent, prior probability distribution on all possible sequences of all possible words over a given alphabet, based on the assumption that the input was generated by concatenating words from a fixed but unknown lexicon. The model is unusual in that it treats the generation of a complete corpus, regardless of length, as a single event in the probability space. Accordingly, the algorithm does not estimate a probability distribution on words; instead, it attempts to calculate the prior probabilities of various word sequences that could underlie the observed text. Experiments on phonemic transcripts of spontaneous speech by parents to young children suggest that this algorithm is more effective than other proposed algorithms, at least when utterance boundaries are given and the text includes a substantial number of short utterances. ::: Keywords: Bayesian grammar induction, probability models, minimum description length (MDL), unsupervised learning, cognitive modeling, language acquisition, segmentation
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paper_title: Automatic extraction of semantic classes from syntactic information in online resources

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Abstract : This paper addresses the issue of word-sense ambiguity in extraction from machine-readable resources for the construction of large-scale knowledge sources. We describe two experiments: one which took word-sense distinctions into account, resulting in 97.9% accuracy for semantic classification of verbs based on (Levin, 1993); and one which ignored word-sense distinctions, resulting in 6.3% accuracy. These experiments were dual purpose: (1) to validate the central thesis of the of (Levin, 1993), i.e., that verb semantics and syntactic behavior are predictably related; (2) to demonstrate that a 20-fold improvement can be achieved in deriving semantic information from syntactic cues if we first divide the syntactic cues into distinct groupings that correlate with different word senses. Finally, we show that we can provide effective acquisition techniques for novel word senses using a combination of online sources.
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paper_title: An Ascription-Based Approach To Speech Acts

paper_content:

The two principal areas of natural language processing research in pragmantics are belief modelling and speech act processing. Belief modelling is the development of techniques to represent the mental attitudes of a dialogue participant. The latter approach, speech act processing, based on speech act theory, involves viewing dialogue in planning terms. Utterances in a dialogue are modelled as steps in a plan where understanding an utterance involves deriving the complete plan a speaker is attempting to achieve. However, previous speech act based approaches have been limited by a reliance upon relatively simplistic belief modelling techniques and their relationship to planning and plan recognition. In particular, such techniques assume precomputed nested belief structures. In this paper, we will present an approach to speech act processing based on novel belief modelling techniques where nested beliefs are propagated on demand.
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paper_title: An investigation of the modified direction feature for cursive character recognition

paper_content:

This paper describes and analyses the performance of a novel feature extraction technique for the recognition of segmented/cursive characters that may be used in the context of a segmentation-based handwritten word recognition system. The modified direction feature (MDF) extraction technique builds upon the direction feature (DF) technique proposed previously that extracts direction information from the structure of character contours. This principal was extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image. In order to improve on the DF extraction technique, a number of modifications were undertaken. With a view to describe the character contour more effectively, a re-design of the direction number determination technique was performed. Also, an additional global feature was introduced to improve the recognition accuracy for those characters that were most frequently confused with patterns of similar appearance. MDF was tested using a neural network-based classifier and compared to the DF and transition feature (TF) extraction techniques. MDF outperformed both DF and TF techniques using a benchmark dataset and compared favourably with the top results in the literature. A recognition accuracy of above 89% is reported on characters from the CEDAR dataset.
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Title: Natural Language Processing: A Survey

Section 1: Introduction
Description 1: Introduce the main goals and components of Natural Language Processing (NLP).

Section 2: History
Description 2: Discuss the origins and early development of NLP, including its association with linguistics, cognitive psychology, and computer science.

Section 3: Why NLP?
Description 3: Explain the importance and benefits of NLP, particularly in terms of user-computer communication.

Section 4: Problems Encountered By NLP
Description 4: Describe the various challenges faced in NLP, detailing specific issues such as syntactic ambiguity, speech segmentation, word sense disambiguation, speech acts problems, and incorrect input.

Section 5: Why Are These Problems So Hard?
Description 5: Explore the reasons why solving problems in NLP is particularly difficult, including the complexities of natural language and resource requirements.

Section 6: Major Applications of NLP
Description 6: Provide an overview of key real-world applications of NLP, such as machine translation, database access, text-to-speech systems, and optical character recognition.

Section 7: Machine Translation
Description 7: Discuss the specifics of machine translation, highlighting its goals, approaches, and challenges.

Section 8: Database Access
Description 8: Explore how NLP is used for database access and information retrieval, including the role of semantic webs.

Section 9: Text-To-Speech Systems
Description 9: Describe text-to-speech systems, their purpose, current capabilities, and ongoing research efforts to improve them.

Section 10: Optical Character Recognition
Description 10: Explain optical character recognition, its benefits, obstacles, past successes, current research, and everyday use.

Section 11: Conclusion
Description 11: Summarize the field of NLP, its current state, ongoing challenges, and potential future developments.