--- title: "Amos Azaria — papers and cards" description: "Amos Azaria: 2 paper cards in the Research library (2018–2021), with what each paper is about, the ideas Mova took from it, and the co-authors." canonical: "https://research.elamur.ai/free/en/authors/amos-azaria/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/amos-azaria/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Amos Azaria # Amos Azaria 2 paper cards in the Research library · 2018–2021 Amos Azaria wrote 2 of the papers Mova reads, in 2018–2021, most of them on [Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/); often with Anusha Prakash, Bishan Yang, Igor Labutov. [OpenAlex](https://openalex.org/A5082567694) · [ORCID](https://orcid.org/0000-0002-5057-1309) · [**h-index 23**](https://openalex.org/authors/A5082567694) · [2616 citations](https://openalex.org/authors/A5082567694) · [158 works](https://openalex.org/works?filter=author.id:A5082567694) — openalex-snapshot, 2026-10-14 The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Amos+Azaria) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Amos+Azaria) · [dblp](https://dblp.org/search?q=Amos+Azaria) · [ACL Anthology](https://aclanthology.org/search/?q=Amos+Azaria) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Amos+Azaria) Topics: [Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/) 1 ## Papers 1. **[Word Sense Induction with Attentive Context Clustering](https://md.research.elamur.ai/free/en/papers/aclbulk-2021-stekel-word-sense-induction-attentive-context/)** *(2021)* ACCWSI induces the senses of an ambiguous lemma without a large pretrained model. A small word2vec model is trained on the corpus; for each sentence with the query lemma, cosine similarity between the lemma vector and each context word gives softmax weights, and the weighted sum is a context-aware vector. These… 2. **[Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds](https://md.research.elamur.ai/free/en/papers/labutov-2018-multi-relational-question-answering-narratives/)** *(2018)* TextWorldsQA is generated, not collected: five simulated worlds (a university, a workplace with meetings, a shopping/chores world and others), each with entity classes and unary-to-ternary relations; every simulation step adds or changes facts and is verbalised as a first-person sentence with varied referring… ## Co-authors (5) - Anusha Prakash - Bishan Yang - Igor Labutov - Moshe Stekel - [Shai Gordin](https://md.research.elamur.ai/free/en/authors/shai-gordin/) ## More papers in the library (1) Pages of the Research library: our summary, the ideas, references and who cites each. - [The Internal State of an LLM Knows When It's Lying](https://md.research.elamur.ai/free/en/papers/azaria-2023-internal-state-llm-knows-when/) — Amos Azaria, Tom Mitchell · 2023