Publication

You can also find my articles on my Google Scholar profile.

Book chapters

1. Ofli, F., Imran, M., & Alam, F. (2020). Using Artificial Intelligence and Social Media for Disaster Response and Management: An Overview. In AI and Robotics in Disaster Studies (pp. 63–81). Springer.
   Abstract BibTeX URL

   @inbook{ofli2020using,
     title = {Using Artificial Intelligence and Social Media for Disaster Response and Management: An Overview},
     author = {Ofli, Ferda and Imran, Muhammad and Alam, Firoj},
     booktitle = {AI and Robotics in Disaster Studies},
     pages = {63--81},
     year = {2020},
     publisher = {Springer},
     note = {https://link.springer.com/chapter/10.1007/978-981-15-4291-6_5}
   }
   

   The ever-increasing popularity of social media platforms has transformed the way in which information is shared during disasters and mass emergencies. Information that emanates from social media, especially in the early hours of a disaster when little-to-no information is available from other traditional sources, can be extremely valuable for emergency responders and decision makers to gain situational awareness and plan relief efforts. To capitalize on this potential, extensive research and development activities have been conducted over the last decade to build technologies to support various humanitarian aid tasks. In this paper, we provide an overview of the literature on using artificial intelligence and social media for disaster response and management from three perspectives: datasets, research studies, and systems. Then, we present further discussion on open research problems and future directions in the crisis informatics domain.
2. Ofli, F., Imran, M., & Alam, F. (2020). Using Artificial Intelligence and Social Media for Disaster Response and Management: An Overview. In AI and Robotics in Disaster Studies (pp. 63–81). Springer.
   Abstract BibTeX URL

   @inbook{ofli2020usinh,
     title = {Using Artificial Intelligence and Social Media for Disaster Response and Management: An Overview},
     author = {Ofli, Ferda and Imran, Muhammad and Alam, Firoj},
     booktitle = {AI and Robotics in Disaster Studies},
     pages = {63--81},
     year = {2020},
     publisher = {Springer},
     note = {https://www.springerprofessional.de/en/using-artificial-intelligence-and-social-media-for-disaster-resp/18475194}
   }
   

   The ever-increasing popularity of social media platforms has transformed the way in which information is shared during disasters and mass emergencies. Information that emanates from social media, especially in the early hours of a disaster when little-to-no information is available from other traditional sources, can be extremely valuable for emergency responders and decision makers to gain situational awareness and plan relief efforts. To capitalize on this potential, extensive research and development activities have been conducted over the last decade to build technologies to support various humanitarian aid tasks. In this paper, we provide an overview of the literature on using artificial intelligence and social media for disaster response and management from three perspectives: datasets, research studies, and systems. Then, we present further discussion on open research problems and future directions in the crisis informatics domain.
3. Alam, F., Danieli, M., & Riccardi, G. (2019). Automatic Labeling Affective Scenes in Spoken Conversations. In Cognitive Infocommunications, Theory and Applications (pp. 109–130). Springer.
   Abstract BibTeX URL

   @inbook{alam2019automatic,
     title = {Automatic Labeling Affective Scenes in Spoken Conversations},
     author = {Alam, Firoj and Danieli, Morena and Riccardi, Giuseppe},
     booktitle = {Cognitive Infocommunications, Theory and Applications},
     pages = {109--130},
     year = {2019},
     publisher = {Springer},
     note = {https://link.springer.com/chapter/10.1007/978-3-319-95996-2_6}
   }
   

   Research in affective computing has mainly focused on analyzing human emotional states as perceivable within limited contexts such as speech utterances. In our study, we focus on the dynamic transitions of the emotional states that are appearing throughout the conversations and investigate computational models to automatically label emotional states using the proposed affective scene framework. An affective scene includes a complete sequence of emotional states in a conversation from its start to its end. Affective scene instances include different patterns of behavior such as who manifests an emotional state, when it is manifested, and which kinds of changes occur due to the influence of one’s emotion onto another interlocutor. In this paper, we present the design and training of an automatic affective scene segmentation and classification system for spoken conversations. We comparatively evaluate the contributions of different feature types in the acoustic, lexical and psycholinguistic space and their correlations and combination.
4. Alam, F., Magnini, B., & Zanoli, R. (2015). Comparing Named Entity Recognition on Transcriptions and Written Texts, Harmonization and Development of Resources and Tools for Italian Natural Language Processing within the PARLI Project. In R. Basili, C. Bosco, R. Delmonte, A. Moschitti, & M. Simi (Eds.), Vol. 589 (Vol. 589). Springer.
   BibTeX

   @inbook{alam2015comparing,
     author = {Alam, Firoj and Magnini, Bernardo and Zanoli, Roberto},
     editor = {Basili, R. and Bosco, C. and Delmonte, R. and Moschitti, A. and Simi, M.},
     journal = {Vol. 589},
     publisher = {Springer},
     title = {Comparing {Named} {Entity} {Recognition} on {Transcriptions} and {Written} {Texts}, {Harmonization} and {Development} of {Resources} and {Tools} for {Italian} {Natural} {Language} {Processing} within the {PARLI} {Project}},
     volume = {589},
     year = {2015}
   }
   
5. Alam, F., & Zanoli, R. (2013). A Combination of Classifiers for Named Entity Recognition on Transcription. In B. Magnini, F. Cutugno, M. Falcone, & E. Pianta (Eds.), Evaluation of Natural Language and Speech Tools for Italian (pp. 107–115). Springer.
   Abstract BibTeX URL

   @inbook{alam2013combination,
     author = {Alam, Firoj and Zanoli, Roberto},
     booktitle = {Evaluation of Natural Language and Speech Tools for Italian},
     editor = {Magnini, Bernardo and Cutugno, Francesco and Falcone, Mauro and Pianta, Emanuele},
     pages = {107--115},
     publisher = {Springer},
     title = {A Combination of Classifiers for Named Entity Recognition on Transcription},
     note = {https://link.springer.com/chapter/10.1007/978-3-642-35828-9_12},
     year = {2013}
   }
   

   This paper presents a Named Entity Recognition (NER) system on broadcast news transcription where two different classifiers are set up in a loop so that the output of one of the classifiers is exploited by the other to refine its decision. The approach we followed is similar to that used in Typhoon, which is a NER system designed for newspaper articles; in that respect, one of the distinguishing features of our approach is the use of Conditional Random Fields in place of Hidden Markov Models. To make the second classifier we extracted sentences from a large unlabelled corpus. Another relevant feature is instead strictly related to transcription annotations. Transcriptions lack orthographic and punctuation information and this typically results in poor performance. As a result, an additional module for case and punctuation restoration has been developed. This paper describes the system and reports its performance which is evaluated by taking part in Evalita 2011 in the task of Named Entity Recognition on Transcribed Broadcast News. In addition, the Evalita 2009 dataset, consisting of newspapers articles, is used to present a comparative analysis by extracting named entities from newspapers and broadcast news.

Journals

1. Alam, F., Alam, T., Imran, M., & Ofli, F. (2021). Robust Training of Social Media Image Classification Models for Rapid Disaster Response.
   Abstract BibTeX URL Code

   @article{alam2021robust,
     title = {Robust Training of Social Media Image Classification Models for Rapid Disaster Response},
     author = {Alam, Firoj and Alam, Tanvirul and Imran, Muhammad and Ofli, Ferda},
     year = {2021},
     eprint = {2104.04184},
     archiveprefix = {arXiv},
     primaryclass = {cs.CV},
     note = {https://arxiv.org/abs/2104.04184},
     code = {}
   }
   

   Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks. As the volume and velocity of such content are typically high, real-time image classification has become an urgent need for a faster disaster response. Recent advances in computer vision and deep neural networks have enabled the development of models for real-time image classification for a number of tasks, including detecting crisis incidents, filtering irrelevant images, classifying images into specific humanitarian categories, and assessing the severity of the damage. To develop robust real-time models, it is necessary to understand the capability of the publicly available pre-trained models for these tasks, which remains to be under-explored in the crisis informatics literature. In this study, we address such limitations by investigating ten different network architectures for four different tasks using the largest publicly available datasets for these tasks. We also explore various data augmentation strategies, semi-supervised techniques, and a multitask learning setup. In our extensive experiments, we achieve promising results.
2. Alam, F., Ofli, F., & Imran, M. (2020). Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria. Behaviour & Information Technology, 39(3), 288–318.
   Abstract BibTeX URL

   @article{alam2020descriptive,
     title = {Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria},
     author = {Alam, Firoj and Ofli, Ferda and Imran, Muhammad},
     journal = {Behaviour \& Information Technology},
     volume = {39},
     number = {3},
     pages = {288--318},
     year = {2020},
     publisher = {Taylor \& Francis},
     note = {https://www.tandfonline.com/doi/full/10.1080/0144929X.2019.1610908}
   }
   

   People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyse high-volume and high-velocity data streams, dealing with information overload, among others. To eliminate such limitations, in this work, we first show that textual and imagery content on social media provide complementary information useful to improve situational awareness. We then explore ways in which various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields can exploit such complementary information generated during disaster events. Finally, we propose a methodological approach that combines several computational techniques effectively in a unified framework to help humanitarian organisations in their relief efforts. We conduct extensive experiments using textual and imagery content from millions of tweets posted during the three major disaster events in the 2017 Atlantic Hurricane season. Our study reveals that the distributions of various types of useful information can inform crisis managers and responders and facilitate the development of future automated systems for disaster management.
3. Alam, F., Danieli, M., & Riccardi, G. (2018). Annotating and modeling empathy in spoken conversations. Computer Speech & Language, 50, 40–61.
   Abstract BibTeX URL

   @article{alam2018annotating,
     title = {Annotating and modeling empathy in spoken conversations},
     author = {Alam, Firoj and Danieli, Morena and Riccardi, Giuseppe},
     journal = {Computer Speech \& Language},
     volume = {50},
     pages = {40--61},
     year = {2018},
     publisher = {Elsevier},
     note = {https://www.sciencedirect.com/science/article/abs/pii/S088523081730133X}
   }
   

   Empathy, as defined in behavioral sciences, expresses the ability of human beings to recognize, understand and react to emotions, attitudes and beliefs of others. In this paper, we address two related problems in automatic affective behavior analysis: the design of the annotation protocol and the automatic recognition of empathy from human–human dyadic spoken conversations. We propose and evaluate an annotation scheme for empathy inspired by the modal model of emotions. The annotation scheme was evaluated on a corpus of real-life, dyadic spoken conversations. In the context of behavioral analysis, we designed an automatic segmentation and classification system for empathy. Given the different speech and language levels of representation where empathy may be communicated, we investigated features derived from the lexical and acoustic spaces. The feature development process was designed to support both the fusion and automatic selection of relevant features from a high dimensional space. The automatic classification system was evaluated on call center conversations where it showed significantly better performance than the baseline.
4. Alam, F., Ofli, F., & Imran, M. (2018). Processing social media images by combining human and machine computing during crises. International Journal of Human–Computer Interaction, 34(4), 311–327.
   Abstract BibTeX URL

   @article{alam2018processing,
     title = {Processing social media images by combining human and machine computing during crises},
     author = {Alam, Firoj and Ofli, Ferda and Imran, Muhammad},
     journal = {International Journal of Human--Computer Interaction},
     volume = {34},
     number = {4},
     pages = {311--327},
     year = {2018},
     publisher = {Taylor \& Francis},
     note = {https://www.tandfonline.com/doi/abs/10.1080/10447318.2018.1427831}
   }
   

   The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness as disaster unfolds. In addition to textual content, people post overwhelming amounts of imagery content on social networks within minutes of a disaster hit. Studies point to the importance of this online imagery content for emergency response. Despite recent advances in computer vision research, making sense of the imagery content in real-time during disasters remains a challenging task. One of the important challenges is that a large proportion of images shared on social media is redundant or irrelevant, which requires robust filtering mechanisms. Another important challenge is that images acquired after major disasters do not share the same characteristics as those in large-scale image collections with clean annotations of well-defined object categories such as house, car, airplane, cat, dog, etc., used traditionally in computer vision research. To tackle these challenges, we present a social media image processing pipeline that combines human and machine intelligence to perform two important tasks: (i) capturing and filtering of social media imagery content (i.e., real-time image streaming, de-duplication, and relevancy filtering); and (ii) actionable information extraction (i.e., damage severity assessment) as a core situational awareness task during an on-going crisis event. Results obtained from extensive experiments on real-world crisis datasets demonstrate the significance of the proposed pipeline for optimal utilization of both human and machine computing resources.
5. Celli, F., Ghosh, A., Alam, F., & Riccardi, G. (2016). In the mood for sharing contents: Emotions, personality and interaction styles in the diffusion of news. Information Processing & Management, 52(1), 93–98. https://doi.org/https://doi.org/10.1016/j.ipm.2015.08.002
   Abstract BibTeX URL DOI

   @article{CELLI201693,
     title = {In the mood for sharing contents: Emotions, personality and interaction styles in the diffusion of news},
     journal = {Information Processing & Management},
     volume = {52},
     number = {1},
     pages = {93-98},
     year = {2016},
     note = {Emotion and Sentiment in Social and Expressive Media},
     issn = {0306-4573},
     doi = {https://doi.org/10.1016/j.ipm.2015.08.002},
     url = {https://www.sciencedirect.com/science/article/pii/S030645731500103X},
     author = {Celli, Fabio and Ghosh, Arindam and Alam, Firoj and Riccardi, Giuseppe},
     keywords = {Personality, Communication, Mood detection, Social media, Twitter, Contagion}
   }
   

   In this paper, we analyze the influence of Twitter users in sharing news articles that may affect the readers’ mood. We collected data of more than 2000 Twitter users who shared news articles from Corriere.it, a daily newspaper that provides mood metadata annotated by readers on a voluntary basis. We automatically annotated personality types and communication styles of Twitter users and analyzed the correlations between personality, communication style, Twitter metadata (such as followig and folllowers) and the type of mood associated to the articles they shared. We also run a feature selection task, to find the best predictors of positive and negative mood sharing, and a classification task. We automatically predicted positive and negative mood sharers with 61.7% F1-measure.
6. Alam, F., Corazza, A., Lavelli, A., & Zanoli, R. (2016). A Knowledge-Poor Approach to Chemical-Disease Relation Extraction. The Journal of Biological Databases and Curation.
   Abstract BibTeX URL

   @article{alamRE2016,
     author = {Alam, Firoj and Corazza, Anna and Lavelli, Alberto and Zanoli, Roberto},
     journal = {The Journal of Biological Databases and Curation},
     title = {A Knowledge-Poor Approach to Chemical-Disease Relation Extraction},
     year = {2016},
     note = {https://academic.oup.com/database/article/doi/10.1093/database/baw071/2630422}
   }
   

   The article describes a knowledge-poor approach to the task of extracting Chemical-Disease Relations from PubMed abstracts. A first version of the approach was applied during the participation in the BioCreative V track 3, both in Disease Named Entity Recognition and Normalization (DNER) and in Chemical-induced diseases (CID) relation extraction. For both tasks, we have adopted a general-purpose approach based on machine learning techniques integrated with a limited number of domain-specific knowledge resources and using freely available tools for preprocessing data. Crucially, the system only uses the data sets provided by the organizers. The aim is to design an easily portable approach with a limited need of domain-specific knowledge resources. In the participation in the BioCreative V task, we ranked 5 out of 16 in DNER, and 7 out of 18 in CID. In this article, we present our follow-up study in particular on CID by performing further experiments, extending our approach and improving the performance.

Conferences/Workshops

1. Alam, F., Qazi, U., Imran, M., & Ofli, F. (2021). HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks. ICWSM.
   BibTeX

   @inproceedings{alam2021humaid,
     title = {{HumAID}: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks},
     author = {Alam, Firoj and Qazi, Umair and Imran, Muhammad and Ofli, Ferda},
     booktitle = {ICWSM},
     year = {2021}
   }
   
2. Alam, F., Sajjad, H., Imran, M., & Ofli, F. (2021). CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing. Proceedings of the International AAAI Conference on Web and Social Media.
   BibTeX URL Code Data

   @inproceedings{alam2020standardizing,
     title = {{CrisisBench}: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing},
     author = {Alam, Firoj and Sajjad, Hassan and Imran, Muhammad and Ofli, Ferda},
     booktitle = {Proceedings of the International AAAI Conference on Web and Social Media},
     year = {2021},
     note = {https://ojs.aaai.org/index.php/ICWSM/article/view/18115},
     code = {https://github.com/firojalam/crisis_datasets_benchmarks},
     data = {https://crisisnlp.qcri.org/crisis_datasets_benchmarks}
   }
   
3. Alam, F., Ofli, F., Imran, M., Alam, T., & Qazi, U. (2020). Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 151–158. https://doi.org/10.1109/ASONAM49781.2020.9381294
   BibTeX DOI

   @inproceedings{FAlam:ASONAM20,
     author = {Alam, Firoj and Ofli, Ferda and Imran, Muhammad and Alam, Tanvirul and Qazi, Umair},
     booktitle = {IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
     title = {Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response},
     year = {2020},
     pages = {151-158},
     doi = {10.1109/ASONAM49781.2020.9381294}
   }
   
4. Imran, M., Alam, F., Qazi, U., Peterson, S., & Ofli, F. (2020). Rapid damage assessment using social media images by combining human and machine intelligence. 17th International Conference on Information Systems for Crisis Response and Management, 761–773.
   BibTeX

   @inproceedings{imran2020rapid,
     title = {Rapid damage assessment using social media images by combining human and machine intelligence},
     author = {Imran, Muhammad and Alam, Firoj and Qazi, Umair and Peterson, Steve and Ofli, Ferda},
     booktitle = {17th International Conference on Information Systems for Crisis Response and Management},
     pages = {761--773},
     year = {2020}
   }
   
5. Ofli, F., Alam, F., & Imran, M. (2020, May). Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response. Proceedings of the Information Systems for Crisis Response and Management.
   BibTeX

   @inproceedings{multimodalbaseline2020,
     author = {Ofli, Ferda and Alam, Firoj and Imran, Muhammad},
     booktitle = {Proceedings of the Information Systems for Crisis Response and Management},
     keywords = {Multimodal deep learning, Multimedia content, Natural disasters, Crisis Computing, Social media},
     month = may,
     title = {Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response},
     year = {2020}
   }
   
6. Alam, F., Muhammad, I., & Ferda, O. (2019). CrisisDPS: Crisis Data Processing Services. Proceedings of the International Conference on Information Systems for Crisis Response and Management (ISCRAM).
   BibTeX

   @inproceedings{Alam2019,
     title = {CrisisDPS: Crisis Data Processing Services},
     author = {Alam, Firoj and Muhammad, Imran and Ferda, Ofli},
     booktitle = {Proceedings of the International Conference on Information Systems for Crisis Response and Management (ISCRAM)},
     year = {2019}
   }
   
7. Alam, F., Joty, S., & Imran, M. (2018). Graph based semi-supervised learning with convolution neural networks to classify crisis related tweets. Proceedings of the International AAAI Conference on Web and Social Media, 12(1).
   BibTeX

   @inproceedings{alam2018graph,
     title = {Graph based semi-supervised learning with convolution neural networks to classify crisis related tweets},
     author = {Alam, Firoj and Joty, Shafiq and Imran, Muhammad},
     booktitle = {Proceedings of the International AAAI Conference on Web and Social Media},
     volume = {12},
     number = {1},
     year = {2018}
   }
   
8. Alam, F., Ofli, F., & Imran, M. (2018). CrisisMMD: Multimodal twitter datasets from natural disasters. Proceedings of the International AAAI Conference on Web and Social Media, 465–473.
   BibTeX

   @inproceedings{alam2018crisismmd,
     title = {{CrisisMMD:} Multimodal twitter datasets from natural disasters},
     author = {Alam, Firoj and Ofli, Ferda and Imran, Muhammad},
     year = {2018},
     month = jun,
     language = {English},
     pages = {465--473},
     booktitle = {Proceedings of the International AAAI Conference on Web and Social Media}
   }
   
9. Alam, F., Ofli, F., & Imran, M. (2018). Processing Social Media Images by Combining Human and Machine Computing during Crises. International Journal of Human Computer Interaction, 34(4), 311–327. https://doi.org/10.1080/10447318.2018.1427831
   BibTeX DOI

   @article{alam2018Image,
     author = {Alam, Firoj and Ofli, Ferda and Imran, Muhammad},
     title = {Processing Social Media Images by Combining Human and Machine Computing during Crises},
     journal = {International Journal of Human Computer Interaction},
     volume = {34},
     number = {4},
     pages = {311-327},
     year = {2018},
     publisher = {Taylor \& Francis},
     doi = {10.1080/10447318.2018.1427831}
   }
   
10. Alam, F., Imran, M., & Ofli, F. (2017). Image4Act: Online Social Media Image Processing for Disaster Response. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 1–4.
    BibTeX

    @inproceedings{alam17demo,
      author = {Alam, Firoj and Imran, Muhammad and Ofli, Ferda},
      booktitle = {Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining},
      pages = {1--4},
      title = {{Image4Act}: Online Social Media Image Processing for Disaster Response.},
      year = {2017}
    }
    
11. Nguyen, D. T., Alam, F., Ofli, F., & Imran, M. (2017, May). Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises. Proc. of ISCRAM.
    BibTeX

    @inproceedings{nguyen2017automatic,
      author = {Nguyen, Dat Tien and Alam, Firoj and Ofli, Ferda and Imran, Muhammad},
      booktitle = {Proc. of {ISCRAM}},
      title = {Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises},
      month = may,
      year = {2017}
    }
    
12. Fabio, C., Giuseppe, R., & Firoj, A. (2016). Multilevel Annotation of Agreement and Disagreement in Italian News Blogs. Proc. of LREC.
    BibTeX

    @inproceedings{Celli2016,
      author = {Fabio, Celli and Giuseppe, Riccardi and Firoj, Alam},
      booktitle = {Proc. of LREC},
      title = {Multilevel Annotation of Agreement and Disagreement in Italian News Blogs},
      year = {2016}
    }
    
13. Danieli, M., Riccardi, G., & Alam, F. (2015). Emotion unfolding and affective scenes: A case study in spoken conversations. Proc. of the International Workshop on Emotion Representations and Modelling for Companion Technologies, 5–11.
    BibTeX

    @inproceedings{danieli2015emotion,
      author = {Danieli, Morena and Riccardi, Giuseppe and Alam, Firoj},
      booktitle = {Proc. of the International Workshop on Emotion Representations and Modelling for Companion Technologies},
      organization = {ACM},
      pages = {5--11},
      title = {Emotion unfolding and affective scenes: A case study in spoken conversations},
      year = {2015}
    }
    
14. Alam, F., Corazza, A., Lavelli, A., & Zanoli, R. (2015). A Knowledge-Poor Approach to BioCreative V DNER and CID Tasks. Proc. of the Fifth BioCreative Challenge Evaluation Workshop, 274–279.
    BibTeX

    @inproceedings{alamRE2015,
      author = {Alam, Firoj and Corazza, Anna and Lavelli, Alberto and Zanoli, Roberto},
      booktitle = {Proc. of the Fifth BioCreative Challenge Evaluation Workshop},
      pages = {274-279},
      title = {A Knowledge-Poor Approach to BioCreative V DNER and CID Tasks},
      year = {2015}
    }
    
15. Stepanov, E., Favre, B., Alam, F., Chowdhury, S., Singla, K., Trione, J., Béchet, F., & Riccardi, G. (2015). Automatic Summarization of Call-center Conversations. Proc. of the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2015).
    BibTeX

    @inproceedings{stepanovautomatic2015,
      author = {Stepanov, E and Favre, B and Alam, Firoj and Chowdhury, S and Singla, K and Trione, J and B{\'e}chet, F and Riccardi, G},
      booktitle = {Proc. of the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2015)},
      title = {Automatic Summarization of Call-center Conversations},
      year = {2015}
    }
    
16. Danieli, M., Riccardi, G., & Alam, F. (2015). Emotion Unfolding and Affective Scenes: A Case Study in Spoken Conversations. Proc. of Emotion Representations and Modelling for Companion Systems (ERM4CT) 2015,
    BibTeX

    @inproceedings{morena2015,
      author = {Danieli, Morena and Riccardi, Giuseppe and Alam, Firoj},
      booktitle = {Proc. of Emotion Representations and Modelling for Companion Systems (ERM4CT) 2015,},
      publisher = {ICMI},
      title = {Emotion Unfolding and Affective Scenes: A Case Study in Spoken Conversations},
      year = {2015}
    }
    
17. Danieli, M., Riccardi, G., & Alam, F. (2014). Annotation of Complex Emotion In Real-Life Dialogues. In R. Basili, A. Lenci, & B. Magnini (Eds.), Proc. of 1st Italian Conf. on Computational Linguistics (CLiC-it) 2014 (Vol. 1, Number 122–127).
    BibTeX

    @inproceedings{morena2014,
      author = {Danieli, Morena and Riccardi, Giuseppe and Alam, Firoj},
      booktitle = {Proc. of 1st Italian Conf. on Computational Linguistics (CLiC-it) 2014},
      editor = {Basili, Roberto and Lenci, Alessandro and Magnini, Bernardo},
      number = {122--127},
      title = {Annotation of Complex Emotion In Real-Life Dialogues},
      volume = {1},
      year = {2014}
    }
    
18. Alam, F., & Riccardi, G. (2014, May). Fusion of Acoustic, Linguistic and Psycholinguistic Features for Speaker Personality Traits Recognition. Proc. of ICASSP2014 - SLTC.
    BibTeX

    @inproceedings{alam2014icassp,
      author = {Alam, Firoj and Riccardi, Giuseppe},
      booktitle = {Proc. of ICASSP2014 - SLTC},
      month = may,
      title = {Fusion of Acoustic, Linguistic and Psycholinguistic Features for Speaker Personality Traits Recognition},
      year = {2014}
    }
    
19. Chowdhury, S. A., Riccardi, G., & Alam, F. (2014). Unsupervised Recognition and Clustering of Speech Overlaps in Spoken Conversations. Proc. of the Workshop on Speech, Language and Audio in Multimedia (SLAM 2014).
    BibTeX

    @inproceedings{chowdhury2014overlap,
      author = {Chowdhury, Shammur A and Riccardi, Giuseppe and Alam, Firoj},
      booktitle = {Proc. of the Workshop on Speech, Language and Audio in Multimedia (SLAM 2014)},
      title = {Unsupervised Recognition and Clustering of Speech Overlaps in Spoken Conversations},
      year = {2014}
    }
    
20. Alam, F., & Riccardi, G. (2014). Predicting Personality Traits using Multimodal Information. Proc. of the 2014 ACM Multi-Media on WCPR14, 15–18.
    BibTeX

    @inproceedings{alam2014predicting,
      author = {Alam, Firoj and Riccardi, Giuseppe},
      booktitle = {Proc. of the 2014 ACM Multi-Media on WCPR14},
      organization = {ACM},
      pages = {15-18},
      title = {Predicting Personality Traits using Multimodal Information},
      year = {2014}
    }
    
21. Danieli, M., Riccardi, G., & Alam, F. (2014). Annotation of Complex Emotions in Real-Life Dialogues: The Case of Empathy. In B. M. Roberto Basili Alessandro Lenci (Ed.), Proc. of the First Italian Conference on Computational Linguistics CLiC-it 2014: Vol. Vol. I.
    BibTeX

    @inproceedings{danieli2014empathyannotation,
      author = {Danieli, Morena and Riccardi, Giuseppe and Alam, Firoj},
      booktitle = {Proc. of the First Italian Conference on Computational Linguistics CLiC-it 2014},
      editor = {Roberto Basili, Alessandro Lenci, Bernardo Magnini},
      month = dec,
      title = {Annotation of Complex Emotions in Real-Life Dialogues: The Case of Empathy},
      volume = {Vol. I},
      year = {2014}
    }
    
22. Alam, F., Stepanov, E. A., & Riccardi, G. (2013). Personality Traits Recognition on Social Network-Facebook. Proc. of the Workshop on Computational Personality Recognition, AAAI Press, Melon Park, CA, 6–9.
    BibTeX

    @inproceedings{alam2013personality,
      author = {Alam, Firoj and Stepanov, Evgeny A and Riccardi, Giuseppe},
      booktitle = {Proc. of the Workshop on Computational Personality Recognition, AAAI Press, Melon Park, CA},
      pages = {6--9},
      title = {Personality Traits Recognition on Social Network-Facebook},
      year = {2013}
    }
    
23. Alam, F., & Riccardi, G. (2013). Comparative Study of Speaker Personality Traits Recognition in Conversational and Broadcast News Speech. Proc. of Interspeech 2013.
    BibTeX

    @inproceedings{alam2013personalityb,
      author = {Alam, Firoj and Riccardi, Giuseppe},
      booktitle = {Proc. of Interspeech 2013},
      title = {Comparative Study of Speaker Personality Traits Recognition in Conversational and Broadcast News Speech},
      year = {2013}
    }
    
24. Alam, F., & Riccardi, G. (2013). Comparative Study of Speaker Personality Traits Recognition in Conversational and Broadcast News Speech. Proc. of Interspeech 2013.
    BibTeX

    @inproceedings{alam2013comparative,
      author = {Alam, Firoj and Riccardi, Giuseppe},
      booktitle = {Proc. of Interspeech 2013},
      read = {0},
      title = {Comparative Study of Speaker Personality Traits Recognition in Conversational and Broadcast News Speech},
      year = {2013}
    }
    
25. Alam, F. (2012). EVALITA 2011: Named Entity Recognition on Transcription using cascaded classifiers. Working Notes of EVALITA 2011.
    BibTeX

    @inproceedings{alam2012ner,
      author = {Alam, Firoj},
      booktitle = {Working Notes of EVALITA 2011},
      title = {EVALITA 2011: Named Entity Recognition on Transcription using cascaded classifiers},
      year = {2012}
    }
    
26. Murtoza, S. M., Alam, F., Sultana, R., Absar, S., & Khan, M. (2011). Phonetically balanced Bangla speech corpus. Proc. of Conference on Human Language Technology for Development.
    BibTeX

    @inproceedings{murtoza2011phonetically,
      author = {Murtoza, SM and Alam, Firoj and Sultana, Rabia and Absar, Shammur and Khan, Mumit},
      booktitle = {Proc. of Conference on Human Language Technology for Development},
      title = {Phonetically balanced Bangla speech corpus},
      year = {2011}
    }
    
27. Alam, F., Habib, S. M. M., & Khan, M. (2011). Bangla Text to Speech using Festival. Proc. of Conference on Human Language Technology for Development (HLTD 2011), Alexandria, Egypt, 02–05.
    BibTeX

    @inproceedings{alam2011bangla,
      author = {Alam, Firoj and Habib, SM Murtoza and Khan, Mumit},
      booktitle = {Proc. of Conference on Human Language Technology for Development (HLTD 2011), Alexandria, Egypt},
      pages = {02--05},
      title = {Bangla Text to Speech using Festival},
      year = {2011}
    }
    
28. Alam, F., Habib, S. M., Sultana, D. A., & Khan, M. (2010). Development of annotated Bangla speech corpora. Proc. of Spoken Language Technologies for Under-Resourced Language.
    BibTeX

    @inproceedings{alam2010development,
      author = {Alam, Firoj and Habib, SM and Sultana, Dil Afroza and Khan, Mumit},
      booktitle = {Proc. of Spoken Language Technologies for Under-resourced language},
      title = {Development of annotated Bangla speech corpora},
      year = {2010}
    }
    
29. Alam, F., Habib, S. M., & Khan, M. (2009). Text normalization system for Bangla. Proc. of Conference on Language and Technology.
    BibTeX

    @inproceedings{alam2008text,
      author = {Alam, Firoj and Habib, SM and Khan, Mumit},
      booktitle = {Proc. of Conference on Language and Technology},
      institution = {Center for research on Bangla language processing (CRBLP), BRAC University},
      title = {Text normalization system for Bangla},
      year = {2009}
    }
    
30. Alam, F., Habib, S. M. M., & Khan, M. (2008). Acoustic Analysis of Bangla Consonants. Proc. of Spoken Language Technologies for Under-Resourced Language (SLTU’08), 5–7.
    BibTeX

    @inproceedings{alam2008acoustic,
      address = {Vietnam},
      author = {Alam, Firoj and Habib, SM Murtoza and Khan, Mumit},
      booktitle = {Proc. of Spoken Language Technologies for Under-resourced language (SLTU'08)},
      pages = {5--7},
      title = {Acoustic Analysis of Bangla Consonants},
      year = {2008}
    }
    
31. Alam, F., Nath, P. K., & Khan, D. M. (2007). Text to speech for Bangla language using festival. Proc. of 1st Intl. Conf. on Digital Comm. and Computer Applications, 1, 853–859.
    BibTeX

    @inproceedings{alam2007text,
      address = {Amman, Jordan},
      author = {Alam, Firoj and Nath, Promila Kanti and Khan, Dr Mumit},
      booktitle = {Proc. of 1st Intl. Conf. on Digital Comm. and Computer Applications},
      pages = {853-859},
      title = {Text to speech for Bangla language using festival},
      volume = {1},
      year = {2007}
    }