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SUMMARY To generate readable captions for Japanese spoken monologues such as lectures in real time, it is necessary to sequentially display captions that have proper linefeeds inserted. This paper proposes a technique for sequentially inserting proper linefeeds into a lecture transcript whenever a bunsetsu, which is a linguistic unit shorter than a sentence in Japanese and that roughly corresponds to a basic phrase in English, is identified. Under the assumption that linefeeds are inserted at bunsetsu boundaries, this technique can reduce the delay time of captioning to the utmost possible. This technique statistically judges whether or not a linefeed should be inserted into each bunsetsu boundary by using the information that is available at the time. We conducted experiments on linefeed insertion using a Japanese lecture corpus. The experimental results confirmed that our method, which is a bunsetsu-based linefeed insertion method, was almost as accurate as the sentence-based linefeed insertion method. In addition, we conducted comparative evaluations using four baseline methods. The results confirmed that our method could insert linefeeds more accurately than the simple methods that are thought to have the same delay time as our method.

The development of a captioning system that supports the real-time understanding of monologue speech such as lectures and commentaries is required. In monologues, since a sentence tends to be long, each sentence is often displayed in multi lines on the screen. In the case, it is necessary to insert linefeeds into a text so that the text becomes easy to read. This paper proposes a rule-based technique for inserting linefeeds into a Japanese spoken monologue sentence as an elemental technique to generate the readable captions. Our method inserts linefeeds into a sentence by applying the rules based on morphemes, dependencies and clause boundaries. We established the rules by circumstantially investigating the corpus annotated with linefeeds. An experiment using Japanese monologue corpus has shown the effectiveness of our rules.

Spoken monologues feature greater sentence length and structural complexity than spoken dialogues. To achieve high-parsing performance for spoken monologues, simplifying the structure by dividing a sentence into suitable language units could prove effective. This paper proposes a method for dependency parsing of Japanese spoken monologues based on sentence segmentation. In this method, dependency parsing is executed in two stages: at the clause level and the sentence level. First, dependencies within a clause are identified by dividing a sentence into clauses and executing stochastic dependency parsing for each clause. Next, dependencies across clause boundaries are identified stochastically, and the dependency structure of the entire sentence is thus completed. An experiment using a spoken monologue corpus shows the effectiveness of this method for efficient dependency parsing of Japanese monologue sentences.

The development of a captioning system that supports the real-time understanding of monologue speech such as lectures and commentaries is required. In monologues, since a sentence tends to be long, each sentence is often displayed in multi lines on the screen and becomes unreadable. In the case, it is necessary to insert linefeeds into a text so that the text becomes easy to read. This paper proposes a technique for inserting linefeeds into a Japanese spoken monologue sentence as an elemental technique to generate the readable captions. Our method inserts linefeeds into a sentence by applying the rules based on morphemes, dependencies and clause boundaries. We established the rules by circumstantially investigating the corpus annotated with linefeeds. An experiment using Japanese monologue corpus has shown the effectiveness of our rules.

To support the real-time understanding of spoken monologue such as lectures and commentaries, the development of a captioning system is required. In monologues, since a sentence tends to be long, each sentence is often displayed in multi lines on one screen, it is necessary to insert linefeeds into a text so that the text becomes easy to read. This paper proposes a technique for inserting linefeeds into a Japanese spoken monologue text as an elemental technique to generate the readable captions. Our method appropriately inserts linefeeds into a sentence by machine learning, based on the information such as dependencies, clause boundaries, pauses and line length. An experiment using Japanese speech data has shown the effectiveness of our technique., P09;1060

Spoken monologues feature greater sentence length and structural complexity than do spoken dialogues. To achieve high parsing performance for spoken monologues, it could prove effective to simplify the structure by dividing a sentence into suitable language units. This paper proposes a method for dependency parsing of Japanese monologues based on sentence segmentation. In this method, the dependency parsing is executed in two stages: at the clause level and the sentence level. First, the dependencies within a clause are identified by dividing a sentence into clauses and executing stochastic dependency parsing for each clause. Next, the dependencies over clause boundaries are identified stochastically, and the dependency structure of the entire sentence is thus completed. An experiment using a spoken monologue corpus shows this method to be effective for efficient dependency parsing of Japanese monologue sentences, P06;1022

This paper proposes a method for reordering words in a Japanese sentence based on concurrent execution with dependency parsing so that the sentence becomes more readable. Our contributions are summarized as follows: (1) we extend a probablistic model used in the previous work which concurrently performs word reordering and dependency parsing; (2) we conducted an evaluation experiment using our semi-automatically constructed evaluation data so that sentences in the data are more likely to be spontaneously written by natives than the automatically constructed evaluation data in the previous work.