activity
20202025
most citedWhen Word Embeddings Become Endangered

13 citations · 63 across the 28 of their papers we have counts for

collaborators

28 papers

cs.CY2025

Threefold model for AI Readiness: A Case Study with Finnish Healthcare SMEs

Mohammed Alnajjar, Khalid Alnajjar, Mika Hämäläinen

This study examines AI adoption among Finnish healthcare SMEs through semi-structured interviews with six health-tech companies. We identify three AI engagement categories: AI-curi…

cs.CL2024

Analyzing Pokémon and Mario Streamers' Twitch Chat with LLM-based User Embeddings

Mika Hämäläinen, Jack Rueter, Khalid Alnajjar

We present a novel digital humanities method for representing our Twitch chatters as user embeddings created by a large language model (LLM). We cluster these embeddings automatica…

cs.CL2024

Leveraging Transformer-Based Models for Predicting Inflection Classes of Words in an Endangered Sami Language

Khalid Alnajjar, Mika Hämäläinen, Jack Rueter

This paper presents a methodology for training a transformer-based model to classify lexical and morphosyntactic features of Skolt Sami, an endangered Uralic language characterized…

cs.CL2023★ 1 cited

Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages

Khalid Alnajjar, Mika Hämäläinen, Jack Rueter

In this paper, we present an approach for translating word embeddings from a majority language into 4 minority languages: Erzya, Moksha, Udmurt and Komi-Zyrian. Furthermore, we ali…

cs.MM2022★ 1 cited

Ring That Bell: A Corpus and Method for Multimodal Metaphor Detection in Videos

Khalid Alnajjar, Mika Hämäläinen, Shuo Zhang

We present the first openly available multimodal metaphor annotated corpus. The corpus consists of videos including audio and subtitles that have been annotated by experts. Further…

cs.CL2022★ 8 cited

Modern French Poetry Generation with RoBERTa and GPT-2

Mika Hämäläinen, Khalid Alnajjar, Thierry Poibeau

We present a novel neural model for modern poetry generation in French. The model consists of two pretrained neural models that are fine-tuned for the poem generation task. The enc…

cs.CL2022

Emotion Conditioned Creative Dialog Generation

Khalid Alnajjar, Mika Hämäläinen

We present a DialGPT based model for generating creative dialog responses that are conditioned based on one of the following emotions: anger, disgust, fear, happiness, pain, sadnes…

cs.CL2022

Automatic Generation of Factual News Headlines in Finnish

Maximilian Koppatz, Khalid Alnajjar, Mika Hämäläinen +1

We present a novel approach to generating news headlines in Finnish for a given news story. We model this as a summarization task where a model is given a news article, and its tas…

cs.CL2022

Video Games as a Corpus: Sentiment Analysis using Fallout New Vegas Dialog

Mika Hämäläinen, Khalid Alnajjar, Thierry Poibeau

We present a method for extracting a multilingual sentiment annotated dialog data set from Fallout New Vegas. The game developers have preannotated every line of dialog in the game…

cs.CL2022★ 2 cited

When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and its Intensity

Khalid Alnajjar, Mika Hämäläinen, Jörg Tiedemann +2

Prerecorded laughter accompanying dialog in comedy TV shows encourages the audience to laugh by clearly marking humorous moments in the show. We present an approach for automatical…

cs.CL2022★ 5 cited

Multilingual Persuasion Detection: Video Games as an Invaluable Data Source for NLP

Teemu Pöyhönen, Mika Hämäläinen, Khalid Alnajjar

Role-playing games (RPGs) have a considerable amount of text in video game dialogues. Quite often this text is semi-annotated by the game developers. In this paper, we extract a mu…

cs.CL2022★ 1 cited

Harnessing Multilingual Resources to Question Answering in Arabic

Khalid Alnajjar, Mika Hämäläinen

The goal of the paper is to predict answers to questions given a passage of Qur'an. The answers are always found in the passage, so the task of the model is to predict where an ans…

cs.CL2021

Processing M.A. Castrén's Materials: Multilingual Typed and Handwritten Manuscripts

Niko Partanen, Jack Rueter, Mika Hämäläinen +1

The study forms a technical report of various tasks that have been performed on the materials collected and published by Finnish ethnographer and linguist, Matthias Alexander Castr…

cs.CL2021★ 1 cited

TFW2V: An Enhanced Document Similarity Method for the Morphologically Rich Finnish Language

Quan Duong, Mika Hämäläinen, Khalid Alnajjar

Measuring the semantic similarity of different texts has many important applications in Digital Humanities research such as information retrieval, document clustering and text summ…

cs.CL2021

Detecting Depression in Thai Blog Posts: a Dataset and a Baseline

Mika Hämäläinen, Pattama Patpong, Khalid Alnajjar +2

We present the first openly available corpus for detecting depression in Thai. Our corpus is compiled by expert verified cases of depression in several online blogs. We experiment…

cs.CL2021★ 1 cited

Finnish Dialect Identification: The Effect of Audio and Text

Mika Hämäläinen, Khalid Alnajjar, Niko Partanen +1

Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We…

cs.CL2021

The Current State of Finnish NLP

Mika Hämäläinen, Khalid Alnajjar

There are a lot of tools and resources available for processing Finnish. In this paper, we survey recent papers focusing on Finnish NLP related to many different subcategories of N…

cs.CL2021

When a Computer Cracks a Joke: Automated Generation of Humorous Headlines

Khalid Alnajjar, Mika Hämäläinen

Automated news generation has become a major interest for new agencies in the past. Oftentimes headlines for such automatically generated news articles are unimaginative as they ha…

cs.CL2021

How Cute is Pikachu? Gathering and Ranking Pokémon Properties from Data with Pokémon Word Embeddings

Mika Hämäläinen, Khalid Alnajjar, Niko Partanen

We present different methods for obtaining descriptive properties automatically for the 151 original Pokémon. We train several different word embeddings models on a crawled Pokémon…

cs.CL2021★ 1 cited

Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers

Mika Hämäläinen, Khalid Alnajjar

We survey human evaluation in papers presenting work on creative natural language generation that have been published in INLG 2020 and ICCC 2020. The most typical human evaluation…

cs.CL2021★ 4 cited

Lemmatization of Historical Old Literary Finnish Texts in Modern Orthography

Mika Hämäläinen, Niko Partanen, Khalid Alnajjar

Texts written in Old Literary Finnish represent the first literary work ever written in Finnish starting from the 16th century. There have been several projects in Finland that hav…

cs.CL2021

Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline

Mika Hämäläinen, Khalid Alnajjar, Niko Partanen +1

This study presents a new dataset on rumor detection in Finnish language news headlines. We have evaluated two different LSTM based models and two different BERT models, and have f…

cs.CL2021

Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered

Mika Hämäläinen, Niko Partanen, Jack Rueter +1

We train neural models for morphological analysis, generation and lemmatization for morphologically rich languages. We present a method for automatically extracting substantially l…

cs.CL2021★ 2 cited

!Qué maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline

Khalid Alnajjar, Mika Hämäläinen

We construct the first ever multimodal sarcasm dataset for Spanish. The audiovisual dataset consists of sarcasm annotated text that is aligned with video and audio. The dataset rep…