The automatic generation of metaphorical expressions helps us write imaginative texts such as poems or novels. This paper proposes a new metaphor generation task, evaluation metrics, and a method to solve the task. Our task is formalized as a problem of finding metaphorical paraphrases for a literal Japanese phrase consisting of a subject, an object, and a verb. We use four evaluation metrics: synonymousness, metaphoricity, novelty, and comprehensibility. Our proposed method generates metaphorical expressions by using three automatically computable scores—similarity, figurativeness, and rarity—corresponding to one of the evaluation metrics. By crowdsourcing, we show how these scores are related to those given by humans in terms of the evaluation metrics and how they are useful in finding human’s preferred expressions in pairwise comparisons.
To communicate with humans in a human-like manner, systems need to understand behavior and psychological states in situations of human-machine interactions, such as in the cases of autonomous driving and nursing robots. We focus on driving situations as they are part of our daily lives and concern safety. To develop such systems, a corpus annotated with behavior and subjectivity in driving situations is necessary. In this study, subjectivity includes emotions, polarity, sentiments, human judgments, perceptions, and cognitions. We construct a driving experience corpus (DEC) (261 blog articles, 8,080 sentences) with four manually annotated tags. First, we annotate spans with driving experience tags (DE). Then, three tags, other’s behavior (OB), self-behavior (SB), and subjectivity (SJ), are annotated within DE spans. In addition to describing the guidelines, we present corpus specifications, agreement between annotators, and three major difficulties during the development: the extended self, important information, and voice in mind. Automatic annotation experiments were conducted on the DEC using Conditional Random Fields-based methods. On the test set, the F-scores were about .55 for both OB and SB and approximately. 75 for SJ, respectively. We provide error analysis that reveals difficulties in interpreting nominatives and differentiating behavior from subjectivity.
all-words 語義曖昧性解消（以下 all-words WSD (word sense disambiguation)）とは文書中のすべての単語の語義ラベルを付与するタスクである．単語の語義は文脈，すなわち周辺の単語によって推定でき，周辺の単語同士が類似している場合中心の単語同士の語義も類似していると考える．そこで本研究では，対象単語とその類義語群から周辺単語の分散表現を作成し，ユークリッド距離を計算することで対象単語の語義を予測した．また，語義の予測結果をもとにコーパスを語義ラベル列に変換し，語義の分散表現を作成した．語義の分散表現を用いて周辺単語ベクトルを作成し直し，再び語義の予測を行った．コーパスには分類語彙表番号がアノテーションされた『現代日本語書き言葉均衡コーパス』(BCCWJ) を利用した．本研究では分類語彙表における分類番号を語義とし，類義語も分類語彙表から取得した．本研究では，提案手法とランダムベースライン，Pseudo Most Frequent Sense (PMFS)，Yarowsky の手法，LDAWN を比較し，提案手法が勝ることを示した．