Titlebook: Artificial Intelligence and Natural Language; 7th International Co Dmitry Ustalov,Andrey Filchenkov,Jan Žižka Conference proceedings 2018 S

农学

Avoiding Echo-Responses in a Retrieval-Based Conversation System context. While the system’s goal is to find the most appropriate response, rather than the most semantically similar one, this tendency results in low-quality responses. We refer to this challenge as the echoing problem. To mitigate this problem, we utilize a hard negative mining approach at the tr

沉着

humectant

Explicit Semantic Analysis as a Means for Topic Labellingut, and the algorithm yields titles of Wikipedia articles that are considered most relevant to the input. An alternative approach that serves as a strong baseline employs titles of first outputs in a search engine, given topic words as a query. In both methods, obtained titles are then automatically

Bouquet

津贴

Cleaning Up After a Party: Post-processing Thesaurus Crowdsourced Dataays. Second, we apply four cluster cleaning techniques based either on word popularity or word embeddings. Evaluation shows that the method based on word embeddings and existing dictionary definitions delivers best results.

enflame

construct

Acoustic Features of Speech of Typically Developing Children Aged 5–16 Yearsf the study is to describe the dynamics of the temporal and spectral characteristics of the words of 5–16 years old children depending on their gender and age. The decrease of stressed and unstressed vowels duration from child’s words to the age of 13 years is revealed. Pitch values of vowels from w

MOAT

Named Entity Recognition in Russian with Word Representation Learned by a Bidirectional Language Modext corpus. We show that these representations can be easily added to existing models and be combined with other word representation features. We evaluate our model on FactRuEval-2016 dataset for named entity recognition in Russian and achieve state of the art results.

ALTER

抛媚眼