Search

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.

Spontaneously spoken Japanese includes a lot of grammatically ill-formed linguistic phenomena such as fillers, hesitations, inversions, and so on, which do not appear in written language. This paper proposes a novel method of robust dependency parsing using a large-scale spoken language corpus, and evaluates the availability and robustness of the method using spontaneously spoken dialogue sentences. By utilizing stochastic information about the appearance of ill-formed phenomena, the method can robustly parse spoken Japanese including fillers, inversions, or dependencies over utterance units. Experimental results reveal that the parsing accuracy reached 87.0 ％, and we confirmed that it is effective to utilize the location information of a bunsetsu, and the distance information between bunsetsus as stochastic information.

Spontaneously spoken Japanese includes a lot of grammatically ill-formed linguistic phenomena such as fillers, hesitations, inversions, and so on, which do not appear in written language. This paper proposes a method of robust dependency parsing using a large-scale spoken language corpus, and evaluates the availability and robustness of the method using spontaneously spoken dialogue sentences. By utilizing stochastic information about the appearance of ill-formed phenomena, the method can robustly parse spoken Japanese including fillers, inversions, or dependencies over utterance units. As a result of an experiment, the parsing accuracy provided 87.0%, and we confirmed that it is effective to utilize the location information of a bunsetsu, and the distance information between bunsetsus as stochastic information., Grant-in-Aids for Young Scientists of the Ministry of Education, Science, Sports and Culture, Japan;The Tatematsu Foundation

Spontaneous speech includes a broad range of linguistic phenomena characteristic of spoken language, and therefore a statistical approach would be effective for robust parsing of spoken language. Though a largescale syntactically annotated corpus is required for the stochastic parsing, its construction requires a lot of human resources. This paper proposes a method of efficiently constructing a spoken language corpus for which the dependency analysis is provided. This method uses an existing spoken language corpus. A stochastic dependency parse is employed to tag spoken language sentences with the dependency structures, and the results are corrected manually. The tagged corpus is constructed in a spiral fashion where in the corrected data is utilized as the statistical information for automatic parsing of other data. Taking this spiral approach reduces the parsing errors, also allowing us to reduce the correction cost. An experiment using 10,995 Japanese utterances shows the spiral approach to be effective for efficient corpus construction.

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

同時通訳や字幕生成のように, 独話を同時的に処理する音声言語処理システムでは, 音声入力にしたがって順次, 解析を実行する漸進的解析技術が必要である.本論文では, 節を解析単位とする独話の漸進的係り受け解析手法を提案する.本手法では, 節境界解析に基づき, 話者による音声入力と同時的に節を同定する.節が入力されるたびにその節の内部の係り受け構造を作成し, すでに入力された節との係り受け関係を動的に決定する.独話文全体が入力される前の段階で係り受け関係を出力することが可能であり, 同時的な音声理解のための言語解析技術として利用できる.独話データを用いた解析実験により, 本手法が, 従来の独話文係り受け解析と同程度の解析性能を備えていることを確認した., In applications of spoken monologue processing such as simultaneous machine interpretation and automatic captions generation, incremental language parsing is strongly required. This paper proposes a technique for incremental dependency parsing of spoken Japanese monologue on a clause-by-clause basis. The technique identifies the clauses based on clause boundaries analysis, analyzes the dependency structures of them, and tries to decide the dependency relations with another clauses, simultaneously with the monologue speech input. The dependency relations are outputted at the stage before the input of the entire monologue sentence, and therefore, our technique can be used for language parsing in simultaneous Japanese speech understanding. An experiment using Japanese monologues has shown that our technique had the same degree of the performance as our past dependency parsing for monologue sentences.

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.