James H. Martin

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MT computers to automate some or all of the process of translating from one language to another. Translation, in its full generality, is a difficult, fascinating, and intensely human endeavor, as rich as any other area of human creativity. Consider the following passage from the end of Chapter 45 of the 18th-century novel The Story of the Stone, also called(More)
In this chapter we turn to tools for interpreting affective meaning, extending our affective study of sentiment analysis in Chapter 7. We use the word ‘affective’, following the tradition in affective computing (Picard, 1995) to mean emotion, sentiment, personality, mood, and attitudes. Affective meaning is closely related to subjectivity, subjectivity the(More)
In this paper, we propose a machine learning algorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algorithm is based on Support Vector Machines which we show give an improvement in performance over earlier classifiers. We show performance improvements through a number of new(More)
The natural language processing community has recently experienced a growth of interest in domain independent shallow semantic parsing—the process of assigning a Who did What to Whom, When, Where, Why, How etc. structure to plain text. This process entails identifying groups of words in a sentence that represent these semantic arguments and assigning(More)
Gracie: Oh yeah. . . and then Mr. and Mrs. Jones were having matrimonial trouble, and my brother was hired to watch Mrs. Jones. George: Well, I imagine she was a very attractive woman. Gracie: She was, and my brother watched her day and night for six months. George: Well, what happened? Gracie: She finally got a divorce. George: Mrs. Jones? Gracie: No, my(More)
Semantic role labeling is the process of annotating the predicate-argument structure in text with semantic labels. In this paper we present a state-of-the-art baseline semantic role labeling system based on Support Vector Machine classifiers. We show improvements on this system by: i) adding new features including features extracted from dependency parses,(More)
This paper describes a semantic role labeling system that uses features derived from different syntactic views, and combines them within a phrase-based chunking paradigm. For an input sentence, syntactic constituent structure parses are generated by a Charniak parser and a Collins parser. Semantic role labels are assigned to the constituents of each parse(More)
We propose and evaluate a linguistically motivated approach to extracting temporal structure necessary to build a timeline. We considered pairs of events in a verb-clause construction, where the first event is a verb and the second event is the head of a clausal argument to that verb. We selected all pairs of events in the TimeBank that participated in(More)
In this paper, we present a semantic role labeler (or chunker) that groups syntactic chunks (i.e. base phrases) into the arguments of a predicate. This is accomplished by casting the semantic labeling as the classification of syntactic chunks (e.g. NP-chunk, PP-chunk) into one of several classes such as the beginning of an argument (B-ARG), inside an(More)