The rule based approach to machine translation (MT) confines grammatical rules between the source and the target language with the goal of constructing grammatical translation between the language pair. In this paper, we describe the structural representation of English stemmer, POS tagging and design transfer rules which can generate Hindi sentence from the structural representation of the Eng...

We describe a natural language parser that uses type information to determine the grammatical structure of simple sentences and phrases. This stands in contrast to studies of type inference where types and grammatical structure play opposite roles, the former being determined by the latter. Our parser is implemented in Haskell and is based on a linguistic theory called applicative universal gra...

This paper explores the contribution of a broad range of syntactic features to WSD: grammatical relations coded as the presence of adjuncts/arguments in isolation or as subcategorization frames, and instantiated grammatical relations between words. We have tested the performance of syntactic features using two different ML algorithms (Decision Lists and AdaBoost) on the Senseval-2 data. Adding ...

This research aimed to investigate ESP oral reading miscues by 3 major groups reading three domain-specific texts from qualitative/quantitative, integrative and direct perspectives via Goodman’s Reading Miscue Inventory (2005, Y.M. Goodman). The three different domain-specific texts were justified as equivalent in terms of readability, vocabulary frequency, rhetorical organization, genre, level...

We present a method for modeling user navigation on a web site using grammatical inference of stochastic regular grammars. With this method we achieve better models than the previously used first order Markov chains, in terms of predictive accuracy and utility of recommendations. In order to obtain comparable results, we apply the same grammatical inference algorithms on Markov chains, modeled ...

In this paper, we present a novel approach to natural language understanding that utilizes context-free grammars (CFGs) in conjunction with sequence-to-sequence (seq2seq) deep learning. Specifically, we take a CFG authored to generate dialogue for our target application, a videogame, and train a long short-term memory (LSTM) recurrent neural network (RNN) to translate the surface utterances tha...

Grammatical constraints impose diverse requirements on the relations between words and phrases in a sentence. Research on the online implementation of grammatical constraints reveals a strikingly uneven profile. The parser shows impressive accuracy in the application of some rather complex constraints, but makes many errors in the implementation of some relatively simple constraints. Just as th...

This paper presents a deterministic dependency parser based on memory-based learning, which parses English text in linear time. When trained and evaluated on the Wall Street Journal section of the Penn Treebank, the parser achieves a maximum attachment score of 87.1%. Unlike most previous systems, the parser produces labeled dependency graphs, using as arc labels a combination of bracket labels...

Morphological analysis is concerned with retrieving the syntactic and morphological properties or the meaning of a morphologically complex word. Morphological analysis retrieves the grammatical features and properties of an inflected word. However, this paper introduces the design and implementation of a Morphological Analyzer for Kokborok, a resource constrained and less computerized Indian la...