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For a system to understand natural language, it needs to be able to takenatural language text and answer questions given in natural language withrespect to that text; it also needs to be able to follow instructions given innatural language. To achieve this, a system must be able to process naturallanguage and be able to capture the knowledge within that text. Thus it needsto be able to translate natural language text into a formal language. Wediscuss our approach to do this, where the translation is achieved by composingthe meaning of words in a sentence. Our initial approach uses an inverse lambdamethod that we developed (and other methods) to learn meaning of words frommeaning of sentences and an initial lexicon. We then present an improved methodwhere the initial lexicon is also learned by analyzing the training sentenceand meaning pairs. We evaluate our methods and compare them with other existingmethods on a corpora of database querying and robot command and control.

Language understanding as a step towards human level intelligence - automatizing the construction of the initial dictionary from example sentences