Computational Linguistics: 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, Hanoi, Vietnam, October 11–13, 2019, Revised Selected Papers

ive Text Summarization Using LSTMs with Rich Features . . . . . . . . 28 Viet Nguyen Quoc, Huong Le Thanh, and Tuan Luu Minh Relation and Word Embedding SemSeq: A Regime for Training Widely-Applicable Word-Sequence Encoders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Hiroaki Tsuyuki, Tetsuji Ogawa, Tetsunori Kobayashi, and Yoshihiko Hayashi Learning to Compose Relational Embeddings in Knowledge Graphs . . . . . . . 56 Wenye Chen, Huda Hakami, and Danushka Bollegala Context-Guided Self-supervised Relation Embeddings . . . . . . . . . . . . . . . . . 67 Huda Hakami and Danushka Bollegala Evaluation of Embedded Vectors for Lexemes and Synsets Toward Expansion of Japanese WordNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Daiki Ko and Koichi Takeuchi Neural Rasch Model: How Do Word Embeddings Adjust Word Difficulty?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Yo Ehara